CGAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example of training an conditional gan on the fashion mnist dataset\n",
    "from numpy import expand_dims\n",
    "from numpy import zeros\n",
    "from numpy import ones\n",
    "from numpy.random import randn\n",
    "from numpy.random import randint\n",
    "from keras.datasets.fashion_mnist import load_data\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Reshape\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import Conv2DTranspose\n",
    "from keras.layers import LeakyReLU\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the standalone discriminator model\n",
    "def define_discriminator(in_shape=(28,28,1), n_classes=10):\n",
    "\t# label input\n",
    "\tin_label = Input(shape=(1,))\n",
    "\t# embedding for categorical input\n",
    "\tli = Embedding(n_classes, 50)(in_label)\n",
    "\t# scale up to image dimensions with linear activation\n",
    "\tn_nodes = in_shape[0] * in_shape[1]\n",
    "\tli = Dense(n_nodes)(li)\n",
    "\t# reshape to additional channel\n",
    "\tli = Reshape((in_shape[0], in_shape[1], 1))(li)\n",
    "\t# image input\n",
    "\tin_image = Input(shape=in_shape)\n",
    "\t# concat label as a channel\n",
    "\tmerge = Concatenate()([in_image, li])\n",
    "\t# downsample\n",
    "\tfe = Conv2D(128, (3,3), strides=(2,2), padding='same')(merge)\n",
    "\tfe = LeakyReLU(alpha=0.2)(fe)\n",
    "\t# downsample\n",
    "\tfe = Conv2D(128, (3,3), strides=(2,2), padding='same')(fe)\n",
    "\tfe = LeakyReLU(alpha=0.2)(fe)\n",
    "\t# flatten feature maps\n",
    "\tfe = Flatten()(fe)\n",
    "\t# dropout\n",
    "\tfe = Dropout(0.4)(fe)\n",
    "\t# output\n",
    "\tout_layer = Dense(1, activation='sigmoid')(fe)\n",
    "\t# define model\n",
    "\tmodel = Model([in_image, in_label], out_layer)\n",
    "\t# compile model\n",
    "\topt = Adam(lr=0.0002, beta_1=0.5)\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1, 50)        500         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 1, 784)       39984       embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 28, 28, 1)    0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 28, 28, 2)    0           input_2[0][0]                    \n",
      "                                                                 reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 14, 14, 128)  2432        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)       (None, 14, 14, 128)  0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 7, 7, 128)    147584      leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)       (None, 7, 7, 128)    0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 6272)         0           leaky_re_lu_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 6272)         0           flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1)            6273        dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 196,773\n",
      "Trainable params: 196,773\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=define_discriminator()\n",
    "model.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utlis.vis_utlis import plot_model\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the standalone generator model\n",
    "def define_generator(latent_dim, n_classes=10):\n",
    "\t# label input\n",
    "\tin_label = Input(shape=(1,))\n",
    "\t# embedding for categorical input\n",
    "\tli = Embedding(n_classes, 50)(in_label)\n",
    "\t# linear multiplication\n",
    "\tn_nodes = 7 * 7\n",
    "\tli = Dense(n_nodes)(li)\n",
    "\t# reshape to additional channel\n",
    "\tli = Reshape((7, 7, 1))(li)\n",
    "\t# image generator input\n",
    "\tin_lat = Input(shape=(latent_dim,))\n",
    "\t# foundation for 7x7 image\n",
    "\tn_nodes = 128 * 7 * 7\n",
    "\tgen = Dense(n_nodes)(in_lat)\n",
    "\tgen = LeakyReLU(alpha=0.2)(gen)\n",
    "\tgen = Reshape((7, 7, 128))(gen)\n",
    "\t# merge image gen and label input\n",
    "\tmerge = Concatenate()([gen, li])\n",
    "\t# upsample to 14x14\n",
    "\tgen = Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(merge)\n",
    "\tgen = LeakyReLU(alpha=0.2)(gen)\n",
    "\t# upsample to 28x28\n",
    "\tgen = Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(gen)\n",
    "\tgen = LeakyReLU(alpha=0.2)(gen)\n",
    "\t# output\n",
    "\tout_layer = Conv2D(1, (7,7), activation='tanh', padding='same')(gen)\n",
    "\t# define model\n",
    "\tmodel = Model([in_lat, in_label], out_layer)\n",
    "\treturn model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            (None, 100)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 6272)         633472      input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 1, 50)        500         input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)       (None, 6272)         0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1, 49)        2499        embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "reshape_3 (Reshape)             (None, 7, 7, 128)    0           leaky_re_lu_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 7, 7, 1)      0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 7, 7, 129)    0           reshape_3[0][0]                  \n",
      "                                                                 reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 14, 14, 128)  264320      concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)       (None, 14, 14, 128)  0           conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 28, 28, 128)  262272      leaky_re_lu_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)       (None, 28, 28, 128)  0           conv2d_transpose_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 28, 28, 1)    6273        leaky_re_lu_5[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 1,169,336\n",
      "Trainable params: 1,169,336\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=define_generator(100,10)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the combined generator and discriminator model, for updating the generator\n",
    "def define_gan(g_model, d_model):\n",
    "\t# make weights in the discriminator not trainable\n",
    "\td_model.trainable = False\n",
    "\t# get noise and label inputs from generator model\n",
    "\tgen_noise, gen_label = g_model.input\n",
    "\t# get image output from the generator model\n",
    "\tgen_output = g_model.output\n",
    "\t# connect image output and label input from generator as inputs to discriminator\n",
    "\tgan_output = d_model([gen_output, gen_label])\n",
    "\t# define gan model as taking noise and label and outputting a classification\n",
    "\tmodel = Model([gen_noise, gen_label], gan_output)\n",
    "\t# compile model\n",
    "\topt = Adam(lr=0.0002, beta_1=0.5)\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer=opt)\n",
    "\treturn model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_66 (InputLayer)           (None, 100)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_65 (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_66 (Dense)                (None, 6272)         633472      input_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "embedding_33 (Embedding)        (None, 1, 50)        500         input_65[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_81 (LeakyReLU)      (None, 6272)         0           dense_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_65 (Dense)                (None, 1, 49)        2499        embedding_33[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "reshape_50 (Reshape)            (None, 7, 7, 128)    0           leaky_re_lu_81[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "reshape_49 (Reshape)            (None, 7, 7, 1)      0           dense_65[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_33 (Concatenate)    (None, 7, 7, 129)    0           reshape_50[0][0]                 \n",
      "                                                                 reshape_49[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_33 (Conv2DTran (None, 14, 14, 128)  264320      concatenate_33[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_82 (LeakyReLU)      (None, 14, 14, 128)  0           conv2d_transpose_33[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_34 (Conv2DTran (None, 28, 28, 128)  262272      leaky_re_lu_82[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_83 (LeakyReLU)      (None, 28, 28, 128)  0           conv2d_transpose_34[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 28, 28, 1)    6273        leaky_re_lu_83[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "model_49 (Model)                (None, 1)            196773      conv2d_49[0][0]                  \n",
      "                                                                 input_65[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 1,366,109\n",
      "Trainable params: 1,169,336\n",
      "Non-trainable params: 196,773\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=define_gan(define_generator(100,10),define_discriminator())\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load fashion mnist images\n",
    "def load_real_samples():\n",
    "\t# load dataset\n",
    "\t(trainX, trainy), (_, _) = load_data()\n",
    "\t# expand to 3d, e.g. add channels\n",
    "\tX = expand_dims(trainX, axis=-1)\n",
    "\t# convert from ints to floats\n",
    "\tX = X.astype('float32')\n",
    "\t# scale from [0,255] to [-1,1]\n",
    "\tX = (X - 127.5) / 127.5\n",
    "\treturn [X, trainy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # select real samples\n",
    "def generate_real_samples(dataset, n_samples):\n",
    "\t# split into images and labels\n",
    "\timages, labels = dataset\n",
    "\t# choose random instances\n",
    "\tix = randint(0, images.shape[0], n_samples)\n",
    "\t# select images and labels\n",
    "\tX, labels = images[ix], labels[ix]\n",
    "\t# generate class labels\n",
    "\ty = ones((n_samples, 1))\n",
    "\treturn [X, labels], y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.96862745],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.92156863],\n",
       "          [-0.5372549 ],\n",
       "          [-0.45882353],\n",
       "          [-1.        ],\n",
       "          [-0.9843137 ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.99215686],\n",
       "          [-0.99215686],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.88235295],\n",
       "          [-0.5058824 ],\n",
       "          [-0.31764707],\n",
       "          [-0.5372549 ],\n",
       "          [-0.43529412],\n",
       "          [-0.6784314 ],\n",
       "          [-1.        ],\n",
       "          [-0.9529412 ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.94509804],\n",
       "          [-0.27058825],\n",
       "          [-0.30980393],\n",
       "          [-0.31764707],\n",
       "          [-0.4745098 ],\n",
       "          [-0.40392157],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.99215686],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.69411767],\n",
       "          [-0.88235295]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.69411767],\n",
       "          [-0.48235294],\n",
       "          [-0.14509805],\n",
       "          [-0.28627452],\n",
       "          [-0.34117648],\n",
       "          [-0.42745098],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.96862745],\n",
       "          [-0.75686276],\n",
       "          [-0.4745098 ],\n",
       "          [-0.90588236]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9843137 ],\n",
       "          [-0.99215686],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9372549 ],\n",
       "          [-0.2       ],\n",
       "          [-0.12941177],\n",
       "          [-0.19215687],\n",
       "          [-0.33333334],\n",
       "          [-0.3254902 ],\n",
       "          [-0.16862746],\n",
       "          [-0.9529412 ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9372549 ],\n",
       "          [-0.5764706 ],\n",
       "          [-0.49803922],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.99215686],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.99215686],\n",
       "          [-0.8666667 ],\n",
       "          [-0.29411766],\n",
       "          [-0.4745098 ],\n",
       "          [-0.14509805],\n",
       "          [-0.3647059 ],\n",
       "          [-0.39607844],\n",
       "          [-0.31764707],\n",
       "          [-0.18431373],\n",
       "          [-0.06666667],\n",
       "          [-0.1764706 ],\n",
       "          [-0.31764707],\n",
       "          [-0.81960785],\n",
       "          [-0.9764706 ],\n",
       "          [-0.7882353 ],\n",
       "          [-0.29411766],\n",
       "          [-0.44313726],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.99215686],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9607843 ],\n",
       "          [-0.78039217],\n",
       "          [-0.5372549 ],\n",
       "          [-0.3647059 ],\n",
       "          [-0.5921569 ],\n",
       "          [-0.49803922],\n",
       "          [-0.01960784],\n",
       "          [-0.7882353 ],\n",
       "          [-0.30980393],\n",
       "          [-0.33333334],\n",
       "          [-0.23921569],\n",
       "          [-0.16078432],\n",
       "          [-0.09803922],\n",
       "          [-0.23137255],\n",
       "          [-0.16862746],\n",
       "          [-0.19215687],\n",
       "          [-0.29411766],\n",
       "          [-0.25490198],\n",
       "          [-0.22352941],\n",
       "          [-0.7490196 ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.99215686],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9843137 ],\n",
       "          [-0.9372549 ],\n",
       "          [-0.48235294],\n",
       "          [-0.24705882],\n",
       "          [-0.48235294],\n",
       "          [-0.6156863 ],\n",
       "          [-0.5294118 ],\n",
       "          [-0.49803922],\n",
       "          [-0.13725491],\n",
       "          [-0.5294118 ],\n",
       "          [-0.33333334],\n",
       "          [-0.13725491],\n",
       "          [-0.3019608 ],\n",
       "          [-0.29411766],\n",
       "          [-0.18431373],\n",
       "          [-0.1764706 ],\n",
       "          [-0.31764707],\n",
       "          [-0.44313726],\n",
       "          [-0.34117648],\n",
       "          [-0.33333334],\n",
       "          [-0.23137255],\n",
       "          [-0.52156866]],\n",
       " \n",
       "         [[-0.9843137 ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.8745098 ],\n",
       "          [-0.62352943],\n",
       "          [-0.64705884],\n",
       "          [-0.5686275 ],\n",
       "          [-0.4117647 ],\n",
       "          [-0.28627452],\n",
       "          [-0.42745098],\n",
       "          [-0.35686275],\n",
       "          [-0.25490198],\n",
       "          [-0.23921569],\n",
       "          [-0.12156863],\n",
       "          [-0.3882353 ],\n",
       "          [-0.67058825],\n",
       "          [-0.12941177],\n",
       "          [-0.34117648],\n",
       "          [-0.29411766],\n",
       "          [-0.14509805],\n",
       "          [-0.06666667],\n",
       "          [-0.34117648],\n",
       "          [-0.5529412 ],\n",
       "          [-0.4745098 ],\n",
       "          [-0.40392157],\n",
       "          [-0.8666667 ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9764706 ],\n",
       "          [-0.8039216 ],\n",
       "          [-0.69411767],\n",
       "          [-0.6       ],\n",
       "          [-0.67058825],\n",
       "          [-0.5921569 ],\n",
       "          [-0.4509804 ],\n",
       "          [-0.43529412],\n",
       "          [-0.4117647 ],\n",
       "          [-0.3882353 ],\n",
       "          [-0.6627451 ],\n",
       "          [-0.20784314],\n",
       "          [-0.14509805],\n",
       "          [-0.78039217],\n",
       "          [ 0.13725491],\n",
       "          [-0.3647059 ],\n",
       "          [-0.2       ],\n",
       "          [-0.40392157],\n",
       "          [-0.5921569 ],\n",
       "          [-0.9529412 ],\n",
       "          [-0.79607844],\n",
       "          [ 0.09803922],\n",
       "          [-0.34117648],\n",
       "          [-0.6       ],\n",
       "          [-0.52156866],\n",
       "          [-0.85882354]],\n",
       " \n",
       "         [[-0.9843137 ],\n",
       "          [-0.7019608 ],\n",
       "          [-0.7019608 ],\n",
       "          [-0.60784316],\n",
       "          [-0.5764706 ],\n",
       "          [-0.54509807],\n",
       "          [-0.5058824 ],\n",
       "          [-0.44313726],\n",
       "          [-0.6156863 ],\n",
       "          [-0.654902  ],\n",
       "          [-0.7176471 ],\n",
       "          [ 0.01960784],\n",
       "          [-0.25490198],\n",
       "          [-0.27058825],\n",
       "          [ 0.03529412],\n",
       "          [-0.6       ],\n",
       "          [-0.44313726],\n",
       "          [-0.45882353],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9843137 ],\n",
       "          [-1.        ],\n",
       "          [-0.88235295],\n",
       "          [-0.5529412 ],\n",
       "          [-0.8666667 ],\n",
       "          [-0.81960785],\n",
       "          [-0.9372549 ]],\n",
       " \n",
       "         [[-0.5137255 ],\n",
       "          [-0.54509807],\n",
       "          [-0.41960785],\n",
       "          [-0.34117648],\n",
       "          [-0.35686275],\n",
       "          [-0.35686275],\n",
       "          [-0.37254903],\n",
       "          [-0.78039217],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.73333335],\n",
       "          [-0.22352941],\n",
       "          [-0.96862745],\n",
       "          [-0.6862745 ],\n",
       "          [-0.4117647 ],\n",
       "          [-1.        ],\n",
       "          [ 0.04313726],\n",
       "          [-0.5372549 ],\n",
       "          [-1.        ],\n",
       "          [-0.8117647 ],\n",
       "          [-0.827451  ],\n",
       "          [-0.8039216 ],\n",
       "          [-0.9529412 ],\n",
       "          [-0.9372549 ],\n",
       "          [-0.85882354],\n",
       "          [-0.84313726],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-0.62352943],\n",
       "          [-0.9137255 ],\n",
       "          [-0.8745098 ],\n",
       "          [-0.88235295],\n",
       "          [-0.90588236],\n",
       "          [-0.8745098 ],\n",
       "          [-0.9764706 ],\n",
       "          [-1.        ],\n",
       "          [-0.90588236],\n",
       "          [-0.92156863],\n",
       "          [-0.92156863],\n",
       "          [-1.        ],\n",
       "          [ 0.15294118],\n",
       "          [-0.09803922],\n",
       "          [-1.        ],\n",
       "          [ 1.        ],\n",
       "          [-0.77254903],\n",
       "          [-0.19215687],\n",
       "          [ 0.49019608],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9843137 ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.90588236],\n",
       "          [-0.8666667 ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-0.8745098 ],\n",
       "          [-0.78039217],\n",
       "          [-0.85882354],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-0.9529412 ],\n",
       "          [-0.8901961 ],\n",
       "          [-0.9372549 ],\n",
       "          [-0.8980392 ],\n",
       "          [-0.8745098 ],\n",
       "          [-1.        ],\n",
       "          [-0.5294118 ],\n",
       "          [ 0.02745098],\n",
       "          [-1.        ],\n",
       "          [-0.35686275],\n",
       "          [-0.11372549],\n",
       "          [-1.        ],\n",
       "          [-0.09019608],\n",
       "          [-0.6392157 ],\n",
       "          [-1.        ],\n",
       "          [-0.9137255 ],\n",
       "          [-0.7176471 ],\n",
       "          [-0.5686275 ],\n",
       "          [-0.5686275 ],\n",
       "          [-0.5764706 ],\n",
       "          [-0.4745098 ],\n",
       "          [-0.9137255 ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]],\n",
       " \n",
       "         [[-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ],\n",
       "          [-1.        ]]]], dtype=float32),\n",
       " array([7], dtype=uint8)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load image data\n",
    "dataset = load_real_samples()\n",
    "generate_real_samples(dataset,1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# generate points in latent space as input for the generator\n",
    "def generate_latent_points(latent_dim, n_samples, n_classes=10):\n",
    "\t# generate points in the latent space\n",
    "\tx_input = randn(latent_dim * n_samples)\n",
    "\t# reshape into a batch of inputs for the network\n",
    "\tz_input = x_input.reshape(n_samples, latent_dim)\n",
    "\t# generate labels\n",
    "\tlabels = randint(0, n_classes, n_samples)\n",
    "\treturn [z_input, labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the generator to generate n fake examples, with class labels\n",
    "def generate_fake_samples(generator, latent_dim, n_samples):\n",
    "\t# generate points in latent space\n",
    "\tz_input, labels_input = generate_latent_points(latent_dim, n_samples)\n",
    "\t# predict outputs\n",
    "\timages = generator.predict([z_input, labels_input])\n",
    "\t# create class labels\n",
    "\ty = zeros((n_samples, 1))\n",
    "\treturn [images, labels_input], y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit\\.conda\\envs\\su\\lib\\site-packages\\keras\\engine\\training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n",
      "C:\\Users\\Sumit\\.conda\\envs\\su\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n",
      "C:\\Users\\Sumit\\.conda\\envs\\su\\lib\\site-packages\\keras\\engine\\training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n",
      "C:\\Users\\Sumit\\.conda\\envs\\su\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, 1/468, d1=0.702, d2=0.694 g=0.692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit\\.conda\\envs\\su\\lib\\site-packages\\keras\\engine\\training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, 2/468, d1=0.645, d2=0.699 g=0.688\n",
      ">1, 3/468, d1=0.579, d2=0.705 g=0.681\n",
      ">1, 4/468, d1=0.535, d2=0.715 g=0.672\n",
      ">1, 5/468, d1=0.471, d2=0.730 g=0.662\n",
      ">1, 6/468, d1=0.444, d2=0.744 g=0.649\n",
      ">1, 7/468, d1=0.383, d2=0.764 g=0.643\n",
      ">1, 8/468, d1=0.357, d2=0.772 g=0.635\n",
      ">1, 9/468, d1=0.319, d2=0.783 g=0.658\n",
      ">1, 10/468, d1=0.318, d2=0.748 g=0.707\n",
      ">1, 11/468, d1=0.357, d2=0.701 g=0.771\n",
      ">1, 12/468, d1=0.302, d2=0.633 g=0.853\n",
      ">1, 13/468, d1=0.341, d2=0.582 g=0.891\n",
      ">1, 14/468, d1=0.327, d2=0.580 g=0.880\n",
      ">1, 15/468, d1=0.282, d2=0.593 g=0.845\n",
      ">1, 16/468, d1=0.260, d2=0.627 g=0.796\n",
      ">1, 17/468, d1=0.258, d2=0.663 g=0.750\n",
      ">1, 18/468, d1=0.279, d2=0.704 g=0.713\n",
      ">1, 19/468, d1=0.236, d2=0.761 g=0.679\n",
      ">1, 20/468, d1=0.220, d2=0.821 g=0.638\n",
      ">1, 21/468, d1=0.181, d2=0.862 g=0.621\n",
      ">1, 22/468, d1=0.167, d2=0.870 g=0.641\n",
      ">1, 23/468, d1=0.128, d2=0.820 g=0.702\n",
      ">1, 24/468, d1=0.114, d2=0.720 g=0.827\n",
      ">1, 25/468, d1=0.112, d2=0.583 g=0.993\n",
      ">1, 26/468, d1=0.097, d2=0.494 g=1.077\n",
      ">1, 27/468, d1=0.084, d2=0.491 g=1.050\n",
      ">1, 28/468, d1=0.080, d2=0.548 g=0.919\n",
      ">1, 29/468, d1=0.074, d2=0.643 g=0.792\n",
      ">1, 30/468, d1=0.065, d2=0.721 g=0.708\n",
      ">1, 31/468, d1=0.047, d2=0.782 g=0.660\n",
      ">1, 32/468, d1=0.070, d2=0.828 g=0.636\n",
      ">1, 33/468, d1=0.052, d2=0.850 g=0.624\n",
      ">1, 34/468, d1=0.054, d2=0.852 g=0.636\n",
      ">1, 35/468, d1=0.079, d2=0.817 g=0.673\n",
      ">1, 36/468, d1=0.109, d2=0.776 g=0.734\n",
      ">1, 37/468, d1=0.098, d2=0.678 g=0.850\n",
      ">1, 38/468, d1=0.117, d2=0.564 g=1.034\n",
      ">1, 39/468, d1=0.128, d2=0.459 g=1.208\n",
      ">1, 40/468, d1=0.179, d2=0.366 g=1.343\n",
      ">1, 41/468, d1=0.192, d2=0.374 g=1.276\n",
      ">1, 42/468, d1=0.211, d2=0.428 g=1.147\n",
      ">1, 43/468, d1=0.195, d2=0.497 g=0.987\n",
      ">1, 44/468, d1=0.172, d2=0.584 g=0.855\n",
      ">1, 45/468, d1=0.205, d2=0.655 g=0.765\n",
      ">1, 46/468, d1=0.144, d2=0.702 g=0.715\n",
      ">1, 47/468, d1=0.133, d2=0.719 g=0.699\n",
      ">1, 48/468, d1=0.085, d2=0.707 g=0.706\n",
      ">1, 49/468, d1=0.102, d2=0.732 g=0.709\n",
      ">1, 50/468, d1=0.096, d2=0.711 g=0.700\n",
      ">1, 51/468, d1=0.074, d2=0.728 g=0.697\n",
      ">1, 52/468, d1=0.130, d2=0.745 g=0.702\n",
      ">1, 53/468, d1=0.073, d2=0.753 g=0.707\n",
      ">1, 54/468, d1=0.150, d2=0.771 g=0.690\n",
      ">1, 55/468, d1=0.125, d2=0.804 g=0.699\n",
      ">1, 56/468, d1=0.180, d2=0.787 g=0.659\n",
      ">1, 57/468, d1=0.163, d2=0.794 g=0.650\n",
      ">1, 58/468, d1=0.153, d2=0.827 g=0.662\n",
      ">1, 59/468, d1=0.152, d2=0.808 g=0.691\n",
      ">1, 60/468, d1=0.141, d2=0.796 g=0.726\n",
      ">1, 61/468, d1=0.167, d2=0.783 g=0.781\n",
      ">1, 62/468, d1=0.302, d2=0.740 g=0.754\n",
      ">1, 63/468, d1=0.251, d2=0.755 g=0.773\n",
      ">1, 64/468, d1=0.270, d2=0.743 g=0.784\n",
      ">1, 65/468, d1=0.289, d2=0.752 g=0.822\n",
      ">1, 66/468, d1=0.378, d2=0.789 g=0.818\n",
      ">1, 67/468, d1=0.388, d2=0.794 g=0.839\n",
      ">1, 68/468, d1=0.400, d2=0.810 g=0.848\n",
      ">1, 69/468, d1=0.480, d2=0.815 g=0.847\n",
      ">1, 70/468, d1=0.549, d2=0.788 g=0.842\n",
      ">1, 71/468, d1=0.604, d2=0.808 g=0.847\n",
      ">1, 72/468, d1=0.608, d2=0.781 g=0.861\n",
      ">1, 73/468, d1=0.617, d2=0.788 g=0.871\n",
      ">1, 74/468, d1=0.625, d2=0.824 g=0.873\n",
      ">1, 75/468, d1=0.780, d2=0.893 g=1.012\n",
      ">1, 76/468, d1=0.740, d2=0.580 g=1.388\n",
      ">1, 77/468, d1=0.803, d2=0.368 g=1.611\n",
      ">1, 78/468, d1=0.860, d2=0.335 g=1.551\n",
      ">1, 79/468, d1=0.705, d2=0.398 g=1.456\n",
      ">1, 80/468, d1=0.714, d2=0.542 g=1.082\n",
      ">1, 81/468, d1=0.595, d2=1.170 g=0.636\n",
      ">1, 82/468, d1=0.679, d2=1.767 g=0.421\n",
      ">1, 83/468, d1=0.788, d2=1.409 g=0.723\n",
      ">1, 84/468, d1=0.606, d2=0.468 g=1.557\n",
      ">1, 85/468, d1=0.459, d2=0.260 g=1.823\n",
      ">1, 86/468, d1=0.342, d2=0.496 g=1.191\n",
      ">1, 87/468, d1=0.416, d2=0.984 g=0.731\n",
      ">1, 88/468, d1=0.360, d2=1.369 g=0.574\n",
      ">1, 89/468, d1=0.460, d2=1.411 g=0.630\n",
      ">1, 90/468, d1=0.747, d2=0.949 g=0.907\n",
      ">1, 91/468, d1=1.043, d2=0.574 g=1.183\n",
      ">1, 92/468, d1=1.095, d2=0.451 g=1.291\n",
      ">1, 93/468, d1=1.062, d2=0.398 g=1.343\n",
      ">1, 94/468, d1=0.912, d2=0.389 g=1.376\n",
      ">1, 95/468, d1=0.860, d2=0.372 g=1.322\n",
      ">1, 96/468, d1=0.776, d2=0.385 g=1.321\n",
      ">1, 97/468, d1=0.756, d2=0.426 g=1.287\n",
      ">1, 98/468, d1=0.624, d2=0.387 g=1.263\n",
      ">1, 99/468, d1=0.682, d2=0.453 g=1.220\n",
      ">1, 100/468, d1=0.703, d2=0.532 g=1.031\n",
      ">1, 101/468, d1=0.696, d2=0.633 g=0.924\n",
      ">1, 102/468, d1=0.658, d2=0.685 g=0.860\n",
      ">1, 103/468, d1=0.666, d2=0.769 g=0.817\n",
      ">1, 104/468, d1=0.712, d2=0.824 g=0.731\n",
      ">1, 105/468, d1=0.746, d2=0.936 g=0.683\n",
      ">1, 106/468, d1=0.747, d2=0.858 g=0.779\n",
      ">1, 107/468, d1=0.736, d2=0.659 g=0.983\n",
      ">1, 108/468, d1=0.676, d2=0.508 g=1.262\n",
      ">1, 109/468, d1=0.591, d2=0.378 g=1.481\n",
      ">1, 110/468, d1=0.545, d2=0.348 g=1.470\n",
      ">1, 111/468, d1=0.511, d2=0.390 g=1.359\n",
      ">1, 112/468, d1=0.454, d2=0.534 g=1.081\n",
      ">1, 113/468, d1=0.490, d2=0.700 g=0.858\n",
      ">1, 114/468, d1=0.568, d2=1.009 g=0.664\n",
      ">1, 115/468, d1=0.635, d2=1.035 g=0.641\n",
      ">1, 116/468, d1=0.815, d2=0.981 g=0.761\n",
      ">1, 117/468, d1=0.855, d2=0.691 g=0.902\n",
      ">1, 118/468, d1=0.879, d2=0.590 g=1.034\n",
      ">1, 119/468, d1=0.874, d2=0.479 g=1.191\n",
      ">1, 120/468, d1=0.829, d2=0.402 g=1.284\n",
      ">1, 121/468, d1=0.816, d2=0.373 g=1.272\n",
      ">1, 122/468, d1=0.760, d2=0.390 g=1.292\n",
      ">1, 123/468, d1=0.671, d2=0.363 g=1.364\n",
      ">1, 124/468, d1=0.648, d2=0.363 g=1.381\n",
      ">1, 125/468, d1=0.633, d2=0.332 g=1.368\n",
      ">1, 126/468, d1=0.651, d2=0.345 g=1.323\n",
      ">1, 127/468, d1=0.598, d2=0.429 g=1.249\n",
      ">1, 128/468, d1=0.629, d2=0.469 g=1.127\n",
      ">1, 129/468, d1=0.656, d2=0.572 g=1.013\n",
      ">1, 130/468, d1=0.560, d2=0.640 g=0.960\n",
      ">1, 131/468, d1=0.597, d2=0.679 g=0.877\n",
      ">1, 132/468, d1=0.655, d2=0.718 g=0.781\n",
      ">1, 133/468, d1=0.647, d2=0.813 g=0.739\n",
      ">1, 134/468, d1=0.614, d2=0.837 g=0.708\n",
      ">1, 135/468, d1=0.682, d2=0.854 g=0.806\n",
      ">1, 136/468, d1=0.637, d2=0.667 g=0.937\n",
      ">1, 137/468, d1=0.648, d2=0.544 g=1.058\n",
      ">1, 138/468, d1=0.561, d2=0.486 g=1.241\n",
      ">1, 139/468, d1=0.567, d2=0.397 g=1.297\n",
      ">1, 140/468, d1=0.507, d2=0.418 g=1.200\n",
      ">1, 141/468, d1=0.578, d2=0.504 g=1.114\n",
      ">1, 142/468, d1=0.565, d2=0.614 g=0.971\n",
      ">1, 143/468, d1=0.484, d2=0.673 g=0.852\n",
      ">1, 144/468, d1=0.559, d2=0.829 g=0.787\n",
      ">1, 145/468, d1=0.643, d2=0.837 g=0.699\n",
      ">1, 146/468, d1=0.605, d2=0.837 g=0.740\n",
      ">1, 147/468, d1=0.656, d2=0.807 g=0.752\n",
      ">1, 148/468, d1=0.685, d2=0.739 g=0.779\n",
      ">1, 149/468, d1=0.748, d2=0.685 g=0.897\n",
      ">1, 150/468, d1=0.702, d2=0.609 g=0.936\n",
      ">1, 151/468, d1=0.712, d2=0.562 g=0.953\n",
      ">1, 152/468, d1=0.722, d2=0.525 g=1.037\n",
      ">1, 153/468, d1=0.736, d2=0.498 g=1.029\n",
      ">1, 154/468, d1=0.691, d2=0.529 g=1.022\n",
      ">1, 155/468, d1=0.750, d2=0.561 g=0.971\n",
      ">1, 156/468, d1=0.674, d2=0.567 g=0.927\n",
      ">1, 157/468, d1=0.692, d2=0.619 g=0.885\n",
      ">1, 158/468, d1=0.658, d2=0.722 g=0.795\n",
      ">1, 159/468, d1=0.701, d2=0.824 g=0.719\n",
      ">1, 160/468, d1=0.654, d2=0.916 g=0.619\n",
      ">1, 161/468, d1=0.627, d2=0.970 g=0.577\n",
      ">1, 162/468, d1=0.724, d2=0.968 g=0.608\n",
      ">1, 163/468, d1=0.683, d2=0.932 g=0.656\n",
      ">1, 164/468, d1=0.684, d2=0.790 g=0.753\n",
      ">1, 165/468, d1=0.568, d2=0.697 g=0.824\n",
      ">1, 166/468, d1=0.627, d2=0.639 g=0.909\n",
      ">1, 167/468, d1=0.592, d2=0.591 g=0.940\n",
      ">1, 168/468, d1=0.553, d2=0.603 g=0.945\n",
      ">1, 169/468, d1=0.544, d2=0.646 g=0.906\n",
      ">1, 170/468, d1=0.518, d2=0.605 g=0.837\n",
      ">1, 171/468, d1=0.538, d2=0.706 g=0.828\n",
      ">1, 172/468, d1=0.560, d2=0.670 g=0.796\n",
      ">1, 173/468, d1=0.539, d2=0.689 g=0.800\n",
      ">1, 174/468, d1=0.571, d2=0.701 g=0.779\n",
      ">1, 175/468, d1=0.592, d2=0.675 g=0.827\n",
      ">1, 176/468, d1=0.590, d2=0.641 g=0.823\n",
      ">1, 177/468, d1=0.603, d2=0.675 g=0.815\n",
      ">1, 178/468, d1=0.668, d2=0.739 g=0.790\n",
      ">1, 179/468, d1=0.655, d2=0.717 g=0.820\n",
      ">1, 180/468, d1=0.644, d2=0.725 g=0.786\n",
      ">1, 181/468, d1=0.699, d2=0.801 g=0.749\n",
      ">1, 182/468, d1=0.644, d2=0.754 g=0.755\n",
      ">1, 183/468, d1=0.695, d2=0.816 g=0.738\n",
      ">1, 184/468, d1=0.667, d2=0.807 g=0.698\n",
      ">1, 185/468, d1=0.745, d2=0.833 g=0.716\n",
      ">1, 186/468, d1=0.693, d2=0.816 g=0.716\n",
      ">1, 187/468, d1=0.765, d2=0.789 g=0.720\n",
      ">1, 188/468, d1=0.732, d2=0.843 g=0.732\n",
      ">1, 189/468, d1=0.702, d2=0.775 g=0.785\n",
      ">1, 190/468, d1=0.728, d2=0.713 g=0.805\n",
      ">1, 191/468, d1=0.740, d2=0.644 g=0.837\n",
      ">1, 192/468, d1=0.719, d2=0.689 g=0.836\n",
      ">1, 193/468, d1=0.661, d2=0.695 g=0.825\n",
      ">1, 194/468, d1=0.704, d2=0.689 g=0.838\n",
      ">1, 195/468, d1=0.721, d2=0.689 g=0.811\n",
      ">1, 196/468, d1=0.686, d2=0.696 g=0.796\n",
      ">1, 197/468, d1=0.702, d2=0.763 g=0.799\n",
      ">1, 198/468, d1=0.697, d2=0.696 g=0.815\n",
      ">1, 199/468, d1=0.678, d2=0.722 g=0.801\n",
      ">1, 200/468, d1=0.685, d2=0.707 g=0.813\n",
      ">1, 201/468, d1=0.710, d2=0.695 g=0.805\n",
      ">1, 202/468, d1=0.711, d2=0.675 g=0.839\n",
      ">1, 203/468, d1=0.710, d2=0.662 g=0.833\n",
      ">1, 204/468, d1=0.721, d2=0.639 g=0.910\n",
      ">1, 205/468, d1=0.728, d2=0.596 g=0.940\n",
      ">1, 206/468, d1=0.739, d2=0.558 g=0.961\n",
      ">1, 207/468, d1=0.701, d2=0.559 g=0.999\n",
      ">1, 208/468, d1=0.715, d2=0.553 g=0.976\n",
      ">1, 209/468, d1=0.698, d2=0.561 g=0.962\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, 210/468, d1=0.677, d2=0.569 g=0.946\n",
      ">1, 211/468, d1=0.642, d2=0.612 g=0.901\n",
      ">1, 212/468, d1=0.638, d2=0.676 g=0.828\n",
      ">1, 213/468, d1=0.664, d2=0.717 g=0.758\n",
      ">1, 214/468, d1=0.675, d2=0.764 g=0.736\n",
      ">1, 215/468, d1=0.666, d2=0.821 g=0.679\n",
      ">1, 216/468, d1=0.645, d2=0.876 g=0.682\n",
      ">1, 217/468, d1=0.710, d2=0.825 g=0.692\n",
      ">1, 218/468, d1=0.694, d2=0.754 g=0.720\n",
      ">1, 219/468, d1=0.672, d2=0.744 g=0.744\n",
      ">1, 220/468, d1=0.659, d2=0.708 g=0.760\n",
      ">1, 221/468, d1=0.667, d2=0.697 g=0.790\n",
      ">1, 222/468, d1=0.662, d2=0.711 g=0.791\n",
      ">1, 223/468, d1=0.671, d2=0.734 g=0.739\n",
      ">1, 224/468, d1=0.641, d2=0.780 g=0.707\n",
      ">1, 225/468, d1=0.686, d2=0.796 g=0.683\n",
      ">1, 226/468, d1=0.655, d2=0.805 g=0.670\n",
      ">1, 227/468, d1=0.717, d2=0.804 g=0.664\n",
      ">1, 228/468, d1=0.671, d2=0.848 g=0.678\n",
      ">1, 229/468, d1=0.701, d2=0.806 g=0.689\n",
      ">1, 230/468, d1=0.744, d2=0.763 g=0.738\n",
      ">1, 231/468, d1=0.757, d2=0.703 g=0.770\n",
      ">1, 232/468, d1=0.741, d2=0.682 g=0.805\n",
      ">1, 233/468, d1=0.703, d2=0.648 g=0.862\n",
      ">1, 234/468, d1=0.736, d2=0.615 g=0.844\n",
      ">1, 235/468, d1=0.745, d2=0.655 g=0.848\n",
      ">1, 236/468, d1=0.700, d2=0.615 g=0.852\n",
      ">1, 237/468, d1=0.701, d2=0.622 g=0.868\n",
      ">1, 238/468, d1=0.708, d2=0.625 g=0.834\n",
      ">1, 239/468, d1=0.712, d2=0.652 g=0.842\n",
      ">1, 240/468, d1=0.656, d2=0.649 g=0.831\n",
      ">1, 241/468, d1=0.679, d2=0.647 g=0.819\n",
      ">1, 242/468, d1=0.671, d2=0.685 g=0.774\n",
      ">1, 243/468, d1=0.658, d2=0.718 g=0.763\n",
      ">1, 244/468, d1=0.656, d2=0.734 g=0.785\n",
      ">1, 245/468, d1=0.700, d2=0.691 g=0.774\n",
      ">1, 246/468, d1=0.689, d2=0.704 g=0.789\n",
      ">1, 247/468, d1=0.663, d2=0.659 g=0.798\n",
      ">1, 248/468, d1=0.664, d2=0.649 g=0.851\n",
      ">1, 249/468, d1=0.668, d2=0.654 g=0.840\n",
      ">1, 250/468, d1=0.685, d2=0.662 g=0.817\n",
      ">1, 251/468, d1=0.687, d2=0.627 g=0.825\n",
      ">1, 252/468, d1=0.686, d2=0.720 g=0.787\n",
      ">1, 253/468, d1=0.627, d2=0.762 g=0.714\n",
      ">1, 254/468, d1=0.646, d2=0.813 g=0.695\n",
      ">1, 255/468, d1=0.671, d2=0.827 g=0.670\n",
      ">1, 256/468, d1=0.731, d2=0.819 g=0.683\n",
      ">1, 257/468, d1=0.736, d2=0.795 g=0.715\n",
      ">1, 258/468, d1=0.744, d2=0.750 g=0.741\n",
      ">1, 259/468, d1=0.722, d2=0.688 g=0.803\n",
      ">1, 260/468, d1=0.738, d2=0.642 g=0.839\n",
      ">1, 261/468, d1=0.692, d2=0.622 g=0.873\n",
      ">1, 262/468, d1=0.748, d2=0.592 g=0.903\n",
      ">1, 263/468, d1=0.711, d2=0.609 g=0.894\n",
      ">1, 264/468, d1=0.740, d2=0.619 g=0.844\n",
      ">1, 265/468, d1=0.683, d2=0.640 g=0.846\n",
      ">1, 266/468, d1=0.682, d2=0.638 g=0.794\n",
      ">1, 267/468, d1=0.664, d2=0.701 g=0.782\n",
      ">1, 268/468, d1=0.648, d2=0.714 g=0.747\n",
      ">1, 269/468, d1=0.621, d2=0.720 g=0.737\n",
      ">1, 270/468, d1=0.666, d2=0.717 g=0.731\n",
      ">1, 271/468, d1=0.628, d2=0.708 g=0.785\n",
      ">1, 272/468, d1=0.628, d2=0.689 g=0.820\n",
      ">1, 273/468, d1=0.656, d2=0.615 g=0.875\n",
      ">1, 274/468, d1=0.625, d2=0.593 g=0.917\n",
      ">1, 275/468, d1=0.587, d2=0.573 g=0.904\n",
      ">1, 276/468, d1=0.613, d2=0.606 g=0.908\n",
      ">1, 277/468, d1=0.659, d2=0.647 g=0.831\n",
      ">1, 278/468, d1=0.597, d2=0.665 g=0.751\n",
      ">1, 279/468, d1=0.602, d2=0.788 g=0.680\n",
      ">1, 280/468, d1=0.659, d2=0.817 g=0.648\n",
      ">1, 281/468, d1=0.620, d2=0.833 g=0.635\n",
      ">1, 282/468, d1=0.690, d2=0.823 g=0.670\n",
      ">1, 283/468, d1=0.734, d2=0.761 g=0.725\n",
      ">1, 284/468, d1=0.692, d2=0.724 g=0.742\n",
      ">1, 285/468, d1=0.692, d2=0.655 g=0.808\n",
      ">1, 286/468, d1=0.716, d2=0.665 g=0.826\n",
      ">1, 287/468, d1=0.695, d2=0.634 g=0.840\n",
      ">1, 288/468, d1=0.736, d2=0.634 g=0.840\n",
      ">1, 289/468, d1=0.698, d2=0.634 g=0.823\n",
      ">1, 290/468, d1=0.731, d2=0.650 g=0.802\n",
      ">1, 291/468, d1=0.668, d2=0.666 g=0.788\n",
      ">1, 292/468, d1=0.693, d2=0.678 g=0.773\n",
      ">1, 293/468, d1=0.658, d2=0.681 g=0.740\n",
      ">1, 294/468, d1=0.652, d2=0.724 g=0.726\n",
      ">1, 295/468, d1=0.700, d2=0.728 g=0.715\n",
      ">1, 296/468, d1=0.685, d2=0.756 g=0.708\n",
      ">1, 297/468, d1=0.731, d2=0.768 g=0.682\n",
      ">1, 298/468, d1=0.702, d2=0.799 g=0.700\n",
      ">1, 299/468, d1=0.699, d2=0.754 g=0.700\n",
      ">1, 300/468, d1=0.737, d2=0.714 g=0.760\n",
      ">1, 301/468, d1=0.675, d2=0.696 g=0.782\n",
      ">1, 302/468, d1=0.690, d2=0.624 g=0.874\n",
      ">1, 303/468, d1=0.707, d2=0.606 g=0.930\n",
      ">1, 304/468, d1=0.682, d2=0.543 g=0.947\n",
      ">1, 305/468, d1=0.658, d2=0.577 g=0.952\n",
      ">1, 306/468, d1=0.671, d2=0.598 g=0.944\n",
      ">1, 307/468, d1=0.682, d2=0.605 g=0.853\n",
      ">1, 308/468, d1=0.662, d2=0.682 g=0.803\n",
      ">1, 309/468, d1=0.625, d2=0.703 g=0.724\n",
      ">1, 310/468, d1=0.643, d2=0.791 g=0.698\n",
      ">1, 311/468, d1=0.618, d2=0.816 g=0.675\n",
      ">1, 312/468, d1=0.647, d2=0.835 g=0.661\n",
      ">1, 313/468, d1=0.684, d2=0.811 g=0.678\n",
      ">1, 314/468, d1=0.675, d2=0.775 g=0.712\n",
      ">1, 315/468, d1=0.702, d2=0.687 g=0.786\n",
      ">1, 316/468, d1=0.688, d2=0.642 g=0.865\n",
      ">1, 317/468, d1=0.695, d2=0.568 g=0.891\n",
      ">1, 318/468, d1=0.698, d2=0.541 g=0.969\n",
      ">1, 319/468, d1=0.678, d2=0.518 g=0.993\n",
      ">1, 320/468, d1=0.659, d2=0.499 g=1.025\n",
      ">1, 321/468, d1=0.664, d2=0.497 g=1.036\n",
      ">1, 322/468, d1=0.689, d2=0.492 g=1.019\n",
      ">1, 323/468, d1=0.679, d2=0.531 g=0.964\n",
      ">1, 324/468, d1=0.708, d2=0.560 g=0.883\n",
      ">1, 325/468, d1=0.677, d2=0.616 g=0.836\n",
      ">1, 326/468, d1=0.672, d2=0.698 g=0.760\n",
      ">1, 327/468, d1=0.661, d2=0.741 g=0.703\n",
      ">1, 328/468, d1=0.645, d2=0.843 g=0.646\n",
      ">1, 329/468, d1=0.609, d2=0.901 g=0.644\n",
      ">1, 330/468, d1=0.639, d2=0.937 g=0.601\n",
      ">1, 331/468, d1=0.723, d2=0.860 g=0.620\n",
      ">1, 332/468, d1=0.657, d2=0.851 g=0.655\n",
      ">1, 333/468, d1=0.689, d2=0.784 g=0.710\n",
      ">1, 334/468, d1=0.671, d2=0.735 g=0.777\n",
      ">1, 335/468, d1=0.682, d2=0.661 g=0.843\n",
      ">1, 336/468, d1=0.686, d2=0.584 g=0.902\n",
      ">1, 337/468, d1=0.694, d2=0.550 g=0.937\n",
      ">1, 338/468, d1=0.649, d2=0.554 g=0.942\n",
      ">1, 339/468, d1=0.634, d2=0.574 g=0.898\n",
      ">1, 340/468, d1=0.635, d2=0.638 g=0.853\n",
      ">1, 341/468, d1=0.665, d2=0.693 g=0.745\n",
      ">1, 342/468, d1=0.666, d2=0.787 g=0.680\n",
      ">1, 343/468, d1=0.653, d2=0.822 g=0.671\n",
      ">1, 344/468, d1=0.664, d2=0.807 g=0.679\n",
      ">1, 345/468, d1=0.664, d2=0.787 g=0.673\n",
      ">1, 346/468, d1=0.682, d2=0.773 g=0.720\n",
      ">1, 347/468, d1=0.664, d2=0.718 g=0.767\n",
      ">1, 348/468, d1=0.708, d2=0.680 g=0.786\n",
      ">1, 349/468, d1=0.700, d2=0.648 g=0.848\n",
      ">1, 350/468, d1=0.725, d2=0.617 g=0.865\n",
      ">1, 351/468, d1=0.707, d2=0.593 g=0.875\n",
      ">1, 352/468, d1=0.677, d2=0.595 g=0.867\n",
      ">1, 353/468, d1=0.689, d2=0.605 g=0.856\n",
      ">1, 354/468, d1=0.646, d2=0.613 g=0.843\n",
      ">1, 355/468, d1=0.676, d2=0.650 g=0.831\n",
      ">1, 356/468, d1=0.717, d2=0.685 g=0.791\n",
      ">1, 357/468, d1=0.728, d2=0.702 g=0.754\n",
      ">1, 358/468, d1=0.655, d2=0.788 g=0.722\n",
      ">1, 359/468, d1=0.713, d2=0.757 g=0.695\n",
      ">1, 360/468, d1=0.702, d2=0.788 g=0.678\n",
      ">1, 361/468, d1=0.748, d2=0.798 g=0.667\n",
      ">1, 362/468, d1=0.675, d2=0.808 g=0.660\n",
      ">1, 363/468, d1=0.684, d2=0.783 g=0.645\n",
      ">1, 364/468, d1=0.690, d2=0.794 g=0.681\n",
      ">1, 365/468, d1=0.673, d2=0.742 g=0.669\n",
      ">1, 366/468, d1=0.687, d2=0.830 g=0.677\n",
      ">1, 367/468, d1=0.719, d2=0.715 g=0.688\n",
      ">1, 368/468, d1=0.729, d2=0.742 g=0.730\n",
      ">1, 369/468, d1=0.703, d2=0.723 g=0.754\n",
      ">1, 370/468, d1=0.693, d2=0.671 g=0.763\n",
      ">1, 371/468, d1=0.683, d2=0.646 g=0.778\n",
      ">1, 372/468, d1=0.674, d2=0.655 g=0.813\n",
      ">1, 373/468, d1=0.668, d2=0.639 g=0.824\n",
      ">1, 374/468, d1=0.681, d2=0.653 g=0.826\n",
      ">1, 375/468, d1=0.680, d2=0.628 g=0.814\n",
      ">1, 376/468, d1=0.641, d2=0.640 g=0.803\n",
      ">1, 377/468, d1=0.690, d2=0.651 g=0.793\n",
      ">1, 378/468, d1=0.642, d2=0.670 g=0.762\n",
      ">1, 379/468, d1=0.658, d2=0.688 g=0.738\n",
      ">1, 380/468, d1=0.638, d2=0.683 g=0.734\n",
      ">1, 381/468, d1=0.666, d2=0.725 g=0.728\n",
      ">1, 382/468, d1=0.668, d2=0.705 g=0.726\n",
      ">1, 383/468, d1=0.695, d2=0.703 g=0.739\n",
      ">1, 384/468, d1=0.653, d2=0.710 g=0.744\n",
      ">1, 385/468, d1=0.686, d2=0.676 g=0.752\n",
      ">1, 386/468, d1=0.690, d2=0.674 g=0.786\n",
      ">1, 387/468, d1=0.665, d2=0.655 g=0.801\n",
      ">1, 388/468, d1=0.690, d2=0.647 g=0.810\n",
      ">1, 389/468, d1=0.685, d2=0.610 g=0.839\n",
      ">1, 390/468, d1=0.700, d2=0.590 g=0.852\n",
      ">1, 391/468, d1=0.670, d2=0.591 g=0.884\n",
      ">1, 392/468, d1=0.646, d2=0.585 g=0.906\n",
      ">1, 393/468, d1=0.675, d2=0.560 g=0.889\n",
      ">1, 394/468, d1=0.666, d2=0.569 g=0.892\n",
      ">1, 395/468, d1=0.698, d2=0.587 g=0.874\n",
      ">1, 396/468, d1=0.667, d2=0.599 g=0.839\n",
      ">1, 397/468, d1=0.632, d2=0.631 g=0.837\n",
      ">1, 398/468, d1=0.638, d2=0.663 g=0.788\n",
      ">1, 399/468, d1=0.693, d2=0.695 g=0.728\n",
      ">1, 400/468, d1=0.617, d2=0.753 g=0.707\n",
      ">1, 401/468, d1=0.610, d2=0.837 g=0.674\n",
      ">1, 402/468, d1=0.665, d2=0.859 g=0.649\n",
      ">1, 403/468, d1=0.684, d2=0.829 g=0.649\n",
      ">1, 404/468, d1=0.734, d2=0.796 g=0.660\n",
      ">1, 405/468, d1=0.710, d2=0.762 g=0.706\n",
      ">1, 406/468, d1=0.659, d2=0.734 g=0.734\n",
      ">1, 407/468, d1=0.686, d2=0.725 g=0.776\n",
      ">1, 408/468, d1=0.701, d2=0.641 g=0.810\n",
      ">1, 409/468, d1=0.713, d2=0.620 g=0.844\n",
      ">1, 410/468, d1=0.683, d2=0.591 g=0.881\n",
      ">1, 411/468, d1=0.682, d2=0.600 g=0.847\n",
      ">1, 412/468, d1=0.645, d2=0.591 g=0.874\n",
      ">1, 413/468, d1=0.655, d2=0.620 g=0.820\n",
      ">1, 414/468, d1=0.644, d2=0.690 g=0.785\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, 415/468, d1=0.656, d2=0.732 g=0.731\n",
      ">1, 416/468, d1=0.685, d2=0.754 g=0.693\n",
      ">1, 417/468, d1=0.674, d2=0.742 g=0.686\n",
      ">1, 418/468, d1=0.702, d2=0.809 g=0.660\n",
      ">1, 419/468, d1=0.720, d2=0.809 g=0.649\n",
      ">1, 420/468, d1=0.684, d2=0.813 g=0.661\n",
      ">1, 421/468, d1=0.741, d2=0.762 g=0.670\n",
      ">1, 422/468, d1=0.733, d2=0.761 g=0.689\n",
      ">1, 423/468, d1=0.716, d2=0.727 g=0.728\n",
      ">1, 424/468, d1=0.765, d2=0.680 g=0.764\n",
      ">1, 425/468, d1=0.718, d2=0.643 g=0.790\n",
      ">1, 426/468, d1=0.746, d2=0.619 g=0.833\n",
      ">1, 427/468, d1=0.716, d2=0.619 g=0.843\n",
      ">1, 428/468, d1=0.683, d2=0.580 g=0.870\n",
      ">1, 429/468, d1=0.703, d2=0.588 g=0.870\n",
      ">1, 430/468, d1=0.680, d2=0.592 g=0.872\n",
      ">1, 431/468, d1=0.704, d2=0.596 g=0.841\n",
      ">1, 432/468, d1=0.680, d2=0.610 g=0.823\n",
      ">1, 433/468, d1=0.753, d2=0.633 g=0.797\n",
      ">1, 434/468, d1=0.654, d2=0.644 g=0.774\n",
      ">1, 435/468, d1=0.686, d2=0.664 g=0.754\n",
      ">1, 436/468, d1=0.648, d2=0.677 g=0.747\n",
      ">1, 437/468, d1=0.679, d2=0.718 g=0.731\n",
      ">1, 438/468, d1=0.672, d2=0.704 g=0.723\n",
      ">1, 439/468, d1=0.684, d2=0.714 g=0.715\n",
      ">1, 440/468, d1=0.671, d2=0.744 g=0.730\n",
      ">1, 441/468, d1=0.675, d2=0.676 g=0.756\n",
      ">1, 442/468, d1=0.706, d2=0.671 g=0.775\n",
      ">1, 443/468, d1=0.659, d2=0.636 g=0.826\n",
      ">1, 444/468, d1=0.649, d2=0.605 g=0.855\n",
      ">1, 445/468, d1=0.647, d2=0.586 g=0.885\n",
      ">1, 446/468, d1=0.656, d2=0.586 g=0.904\n",
      ">1, 447/468, d1=0.665, d2=0.555 g=0.942\n",
      ">1, 448/468, d1=0.645, d2=0.564 g=0.921\n",
      ">1, 449/468, d1=0.622, d2=0.561 g=0.908\n",
      ">1, 450/468, d1=0.661, d2=0.575 g=0.881\n",
      ">1, 451/468, d1=0.626, d2=0.592 g=0.838\n",
      ">1, 452/468, d1=0.639, d2=0.634 g=0.813\n",
      ">1, 453/468, d1=0.645, d2=0.658 g=0.789\n",
      ">1, 454/468, d1=0.645, d2=0.716 g=0.716\n",
      ">1, 455/468, d1=0.608, d2=0.736 g=0.720\n",
      ">1, 456/468, d1=0.653, d2=0.730 g=0.696\n",
      ">1, 457/468, d1=0.663, d2=0.721 g=0.705\n",
      ">1, 458/468, d1=0.678, d2=0.736 g=0.718\n",
      ">1, 459/468, d1=0.667, d2=0.721 g=0.733\n",
      ">1, 460/468, d1=0.666, d2=0.692 g=0.752\n",
      ">1, 461/468, d1=0.688, d2=0.667 g=0.787\n",
      ">1, 462/468, d1=0.677, d2=0.643 g=0.793\n",
      ">1, 463/468, d1=0.673, d2=0.620 g=0.820\n",
      ">1, 464/468, d1=0.636, d2=0.618 g=0.818\n",
      ">1, 465/468, d1=0.661, d2=0.593 g=0.824\n",
      ">1, 466/468, d1=0.650, d2=0.589 g=0.846\n",
      ">1, 467/468, d1=0.629, d2=0.593 g=0.848\n",
      ">1, 468/468, d1=0.619, d2=0.603 g=0.840\n",
      ">2, 1/468, d1=0.641, d2=0.619 g=0.837\n",
      ">2, 2/468, d1=0.665, d2=0.630 g=0.804\n",
      ">2, 3/468, d1=0.622, d2=0.649 g=0.787\n",
      ">2, 4/468, d1=0.651, d2=0.665 g=0.772\n",
      ">2, 5/468, d1=0.610, d2=0.710 g=0.741\n",
      ">2, 6/468, d1=0.625, d2=0.700 g=0.731\n",
      ">2, 7/468, d1=0.644, d2=0.731 g=0.727\n",
      ">2, 8/468, d1=0.650, d2=0.768 g=0.704\n",
      ">2, 9/468, d1=0.625, d2=0.774 g=0.712\n",
      ">2, 10/468, d1=0.641, d2=0.709 g=0.706\n",
      ">2, 11/468, d1=0.666, d2=0.728 g=0.710\n",
      ">2, 12/468, d1=0.663, d2=0.741 g=0.731\n",
      ">2, 13/468, d1=0.666, d2=0.690 g=0.763\n",
      ">2, 14/468, d1=0.665, d2=0.728 g=0.750\n",
      ">2, 15/468, d1=0.640, d2=0.671 g=0.805\n",
      ">2, 16/468, d1=0.654, d2=0.665 g=0.822\n",
      ">2, 17/468, d1=0.679, d2=0.672 g=0.822\n",
      ">2, 18/468, d1=0.652, d2=0.642 g=0.808\n",
      ">2, 19/468, d1=0.655, d2=0.681 g=0.791\n",
      ">2, 20/468, d1=0.645, d2=0.745 g=0.743\n",
      ">2, 21/468, d1=0.661, d2=0.772 g=0.694\n",
      ">2, 22/468, d1=0.647, d2=0.814 g=0.660\n",
      ">2, 23/468, d1=0.641, d2=0.823 g=0.630\n",
      ">2, 24/468, d1=0.630, d2=0.853 g=0.615\n",
      ">2, 25/468, d1=0.650, d2=0.850 g=0.607\n",
      ">2, 26/468, d1=0.676, d2=0.839 g=0.649\n",
      ">2, 27/468, d1=0.653, d2=0.767 g=0.662\n",
      ">2, 28/468, d1=0.664, d2=0.749 g=0.694\n",
      ">2, 29/468, d1=0.681, d2=0.732 g=0.715\n",
      ">2, 30/468, d1=0.669, d2=0.678 g=0.764\n",
      ">2, 31/468, d1=0.669, d2=0.663 g=0.778\n",
      ">2, 32/468, d1=0.684, d2=0.639 g=0.818\n",
      ">2, 33/468, d1=0.689, d2=0.635 g=0.800\n",
      ">2, 34/468, d1=0.647, d2=0.650 g=0.800\n",
      ">2, 35/468, d1=0.696, d2=0.660 g=0.796\n",
      ">2, 36/468, d1=0.651, d2=0.656 g=0.779\n",
      ">2, 37/468, d1=0.614, d2=0.689 g=0.757\n",
      ">2, 38/468, d1=0.642, d2=0.710 g=0.703\n",
      ">2, 39/468, d1=0.622, d2=0.730 g=0.674\n",
      ">2, 40/468, d1=0.634, d2=0.764 g=0.654\n",
      ">2, 41/468, d1=0.647, d2=0.792 g=0.636\n",
      ">2, 42/468, d1=0.581, d2=0.788 g=0.658\n",
      ">2, 43/468, d1=0.627, d2=0.823 g=0.646\n",
      ">2, 44/468, d1=0.638, d2=0.780 g=0.686\n",
      ">2, 45/468, d1=0.632, d2=0.773 g=0.683\n",
      ">2, 46/468, d1=0.603, d2=0.731 g=0.712\n",
      ">2, 47/468, d1=0.641, d2=0.727 g=0.743\n",
      ">2, 48/468, d1=0.629, d2=0.690 g=0.773\n",
      ">2, 49/468, d1=0.625, d2=0.697 g=0.783\n",
      ">2, 50/468, d1=0.603, d2=0.693 g=0.785\n",
      ">2, 51/468, d1=0.626, d2=0.653 g=0.783\n",
      ">2, 52/468, d1=0.588, d2=0.676 g=0.787\n",
      ">2, 53/468, d1=0.586, d2=0.680 g=0.785\n",
      ">2, 54/468, d1=0.593, d2=0.665 g=0.787\n",
      ">2, 55/468, d1=0.590, d2=0.661 g=0.783\n",
      ">2, 56/468, d1=0.627, d2=0.672 g=0.763\n",
      ">2, 57/468, d1=0.619, d2=0.665 g=0.784\n",
      ">2, 58/468, d1=0.615, d2=0.677 g=0.768\n",
      ">2, 59/468, d1=0.614, d2=0.665 g=0.767\n",
      ">2, 60/468, d1=0.614, d2=0.670 g=0.782\n",
      ">2, 61/468, d1=0.624, d2=0.681 g=0.786\n",
      ">2, 62/468, d1=0.621, d2=0.669 g=0.780\n",
      ">2, 63/468, d1=0.611, d2=0.653 g=0.797\n",
      ">2, 64/468, d1=0.651, d2=0.648 g=0.803\n",
      ">2, 65/468, d1=0.642, d2=0.640 g=0.801\n",
      ">2, 66/468, d1=0.653, d2=0.628 g=0.808\n",
      ">2, 67/468, d1=0.636, d2=0.608 g=0.833\n",
      ">2, 68/468, d1=0.657, d2=0.621 g=0.831\n",
      ">2, 69/468, d1=0.632, d2=0.620 g=0.836\n",
      ">2, 70/468, d1=0.612, d2=0.602 g=0.811\n",
      ">2, 71/468, d1=0.630, d2=0.628 g=0.821\n",
      ">2, 72/468, d1=0.616, d2=0.639 g=0.800\n",
      ">2, 73/468, d1=0.653, d2=0.646 g=0.762\n",
      ">2, 74/468, d1=0.625, d2=0.665 g=0.761\n",
      ">2, 75/468, d1=0.642, d2=0.698 g=0.756\n",
      ">2, 76/468, d1=0.655, d2=0.702 g=0.711\n",
      ">2, 77/468, d1=0.576, d2=0.733 g=0.718\n",
      ">2, 78/468, d1=0.669, d2=0.727 g=0.698\n",
      ">2, 79/468, d1=0.655, d2=0.774 g=0.720\n",
      ">2, 80/468, d1=0.646, d2=0.744 g=0.693\n",
      ">2, 81/468, d1=0.661, d2=0.718 g=0.712\n",
      ">2, 82/468, d1=0.630, d2=0.738 g=0.712\n",
      ">2, 83/468, d1=0.666, d2=0.738 g=0.736\n",
      ">2, 84/468, d1=0.637, d2=0.662 g=0.759\n",
      ">2, 85/468, d1=0.630, d2=0.703 g=0.758\n",
      ">2, 86/468, d1=0.641, d2=0.663 g=0.758\n",
      ">2, 87/468, d1=0.666, d2=0.656 g=0.773\n",
      ">2, 88/468, d1=0.612, d2=0.658 g=0.776\n",
      ">2, 89/468, d1=0.649, d2=0.703 g=0.775\n",
      ">2, 90/468, d1=0.689, d2=0.674 g=0.754\n",
      ">2, 91/468, d1=0.649, d2=0.703 g=0.748\n",
      ">2, 92/468, d1=0.635, d2=0.671 g=0.738\n",
      ">2, 93/468, d1=0.607, d2=0.678 g=0.751\n",
      ">2, 94/468, d1=0.626, d2=0.707 g=0.752\n",
      ">2, 95/468, d1=0.636, d2=0.695 g=0.749\n",
      ">2, 96/468, d1=0.630, d2=0.683 g=0.760\n",
      ">2, 97/468, d1=0.651, d2=0.712 g=0.743\n",
      ">2, 98/468, d1=0.621, d2=0.663 g=0.763\n",
      ">2, 99/468, d1=0.641, d2=0.662 g=0.797\n",
      ">2, 100/468, d1=0.683, d2=0.642 g=0.803\n",
      ">2, 101/468, d1=0.649, d2=0.644 g=0.810\n",
      ">2, 102/468, d1=0.683, d2=0.634 g=0.821\n",
      ">2, 103/468, d1=0.659, d2=0.607 g=0.827\n",
      ">2, 104/468, d1=0.661, d2=0.640 g=0.812\n",
      ">2, 105/468, d1=0.647, d2=0.664 g=0.793\n",
      ">2, 106/468, d1=0.622, d2=0.671 g=0.790\n",
      ">2, 107/468, d1=0.663, d2=0.654 g=0.806\n",
      ">2, 108/468, d1=0.646, d2=0.674 g=0.762\n",
      ">2, 109/468, d1=0.664, d2=0.678 g=0.740\n",
      ">2, 110/468, d1=0.653, d2=0.702 g=0.724\n",
      ">2, 111/468, d1=0.685, d2=0.709 g=0.713\n",
      ">2, 112/468, d1=0.630, d2=0.716 g=0.725\n",
      ">2, 113/468, d1=0.647, d2=0.743 g=0.755\n",
      ">2, 114/468, d1=0.630, d2=0.685 g=0.773\n",
      ">2, 115/468, d1=0.625, d2=0.686 g=0.784\n",
      ">2, 116/468, d1=0.614, d2=0.646 g=0.801\n",
      ">2, 117/468, d1=0.619, d2=0.635 g=0.815\n",
      ">2, 118/468, d1=0.634, d2=0.658 g=0.822\n",
      ">2, 119/468, d1=0.591, d2=0.592 g=0.844\n",
      ">2, 120/468, d1=0.633, d2=0.596 g=0.829\n",
      ">2, 121/468, d1=0.616, d2=0.607 g=0.825\n",
      ">2, 122/468, d1=0.618, d2=0.627 g=0.850\n",
      ">2, 123/468, d1=0.608, d2=0.611 g=0.842\n",
      ">2, 124/468, d1=0.571, d2=0.599 g=0.827\n",
      ">2, 125/468, d1=0.635, d2=0.636 g=0.783\n",
      ">2, 126/468, d1=0.617, d2=0.652 g=0.805\n",
      ">2, 127/468, d1=0.640, d2=0.680 g=0.772\n",
      ">2, 128/468, d1=0.610, d2=0.665 g=0.781\n",
      ">2, 129/468, d1=0.649, d2=0.719 g=0.773\n",
      ">2, 130/468, d1=0.631, d2=0.687 g=0.773\n",
      ">2, 131/468, d1=0.612, d2=0.659 g=0.767\n",
      ">2, 132/468, d1=0.659, d2=0.657 g=0.782\n",
      ">2, 133/468, d1=0.659, d2=0.647 g=0.809\n",
      ">2, 134/468, d1=0.657, d2=0.648 g=0.820\n",
      ">2, 135/468, d1=0.621, d2=0.638 g=0.817\n",
      ">2, 136/468, d1=0.648, d2=0.626 g=0.877\n",
      ">2, 137/468, d1=0.616, d2=0.622 g=0.835\n",
      ">2, 138/468, d1=0.671, d2=0.589 g=0.847\n",
      ">2, 139/468, d1=0.705, d2=0.641 g=0.825\n",
      ">2, 140/468, d1=0.625, d2=0.622 g=0.831\n",
      ">2, 141/468, d1=0.621, d2=0.670 g=0.828\n",
      ">2, 142/468, d1=0.623, d2=0.636 g=0.803\n",
      ">2, 143/468, d1=0.644, d2=0.688 g=0.785\n",
      ">2, 144/468, d1=0.617, d2=0.695 g=0.758\n",
      ">2, 145/468, d1=0.616, d2=0.682 g=0.764\n",
      ">2, 146/468, d1=0.634, d2=0.698 g=0.749\n",
      ">2, 147/468, d1=0.636, d2=0.690 g=0.752\n",
      ">2, 148/468, d1=0.624, d2=0.679 g=0.743\n",
      ">2, 149/468, d1=0.662, d2=0.672 g=0.759\n",
      ">2, 150/468, d1=0.587, d2=0.667 g=0.788\n",
      ">2, 151/468, d1=0.569, d2=0.661 g=0.772\n",
      ">2, 152/468, d1=0.682, d2=0.630 g=0.797\n",
      ">2, 153/468, d1=0.627, d2=0.630 g=0.795\n",
      ">2, 154/468, d1=0.604, d2=0.633 g=0.830\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">2, 155/468, d1=0.592, d2=0.629 g=0.809\n",
      ">2, 156/468, d1=0.580, d2=0.665 g=0.768\n",
      ">2, 157/468, d1=0.603, d2=0.649 g=0.803\n",
      ">2, 158/468, d1=0.568, d2=0.641 g=0.795\n",
      ">2, 159/468, d1=0.610, d2=0.712 g=0.801\n",
      ">2, 160/468, d1=0.606, d2=0.690 g=0.791\n",
      ">2, 161/468, d1=0.623, d2=0.669 g=0.779\n",
      ">2, 162/468, d1=0.649, d2=0.695 g=0.777\n",
      ">2, 163/468, d1=0.656, d2=0.669 g=0.812\n",
      ">2, 164/468, d1=0.582, d2=0.639 g=0.838\n",
      ">2, 165/468, d1=0.595, d2=0.624 g=0.837\n",
      ">2, 166/468, d1=0.590, d2=0.623 g=0.843\n",
      ">2, 167/468, d1=0.602, d2=0.597 g=0.885\n",
      ">2, 168/468, d1=0.592, d2=0.581 g=0.875\n",
      ">2, 169/468, d1=0.584, d2=0.582 g=0.870\n",
      ">2, 170/468, d1=0.607, d2=0.611 g=0.853\n",
      ">2, 171/468, d1=0.609, d2=0.643 g=0.837\n",
      ">2, 172/468, d1=0.607, d2=0.635 g=0.790\n",
      ">2, 173/468, d1=0.554, d2=0.649 g=0.776\n",
      ">2, 174/468, d1=0.595, d2=0.659 g=0.768\n",
      ">2, 175/468, d1=0.565, d2=0.710 g=0.762\n",
      ">2, 176/468, d1=0.568, d2=0.704 g=0.758\n",
      ">2, 177/468, d1=0.586, d2=0.726 g=0.759\n",
      ">2, 178/468, d1=0.588, d2=0.673 g=0.772\n",
      ">2, 179/468, d1=0.561, d2=0.677 g=0.812\n",
      ">2, 180/468, d1=0.551, d2=0.663 g=0.797\n",
      ">2, 181/468, d1=0.530, d2=0.651 g=0.844\n",
      ">2, 182/468, d1=0.584, d2=0.633 g=0.832\n",
      ">2, 183/468, d1=0.554, d2=0.619 g=0.810\n",
      ">2, 184/468, d1=0.548, d2=0.650 g=0.828\n",
      ">2, 185/468, d1=0.562, d2=0.649 g=0.816\n",
      ">2, 186/468, d1=0.512, d2=0.626 g=0.780\n",
      ">2, 187/468, d1=0.528, d2=0.635 g=0.778\n",
      ">2, 188/468, d1=0.521, d2=0.656 g=0.802\n",
      ">2, 189/468, d1=0.555, d2=0.656 g=0.822\n",
      ">2, 190/468, d1=0.533, d2=0.634 g=0.836\n",
      ">2, 191/468, d1=0.578, d2=0.672 g=0.829\n",
      ">2, 192/468, d1=0.572, d2=0.610 g=0.888\n",
      ">2, 193/468, d1=0.552, d2=0.635 g=0.908\n",
      ">2, 194/468, d1=0.581, d2=0.578 g=0.888\n",
      ">2, 195/468, d1=0.594, d2=0.560 g=0.934\n",
      ">2, 196/468, d1=0.603, d2=0.573 g=0.906\n",
      ">2, 197/468, d1=0.561, d2=0.544 g=0.908\n",
      ">2, 198/468, d1=0.634, d2=0.587 g=0.885\n",
      ">2, 199/468, d1=0.582, d2=0.590 g=0.892\n",
      ">2, 200/468, d1=0.534, d2=0.606 g=0.847\n",
      ">2, 201/468, d1=0.523, d2=0.601 g=0.887\n",
      ">2, 202/468, d1=0.581, d2=0.673 g=0.848\n",
      ">2, 203/468, d1=0.550, d2=0.641 g=0.806\n",
      ">2, 204/468, d1=0.545, d2=0.687 g=0.824\n",
      ">2, 205/468, d1=0.508, d2=0.650 g=0.857\n",
      ">2, 206/468, d1=0.558, d2=0.659 g=0.872\n",
      ">2, 207/468, d1=0.533, d2=0.676 g=0.866\n",
      ">2, 208/468, d1=0.558, d2=0.631 g=0.884\n",
      ">2, 209/468, d1=0.580, d2=0.614 g=0.888\n",
      ">2, 210/468, d1=0.560, d2=0.582 g=0.880\n",
      ">2, 211/468, d1=0.546, d2=0.601 g=0.900\n",
      ">2, 212/468, d1=0.557, d2=0.598 g=0.873\n",
      ">2, 213/468, d1=0.509, d2=0.620 g=0.857\n",
      ">2, 214/468, d1=0.587, d2=0.633 g=0.837\n",
      ">2, 215/468, d1=0.515, d2=0.622 g=0.824\n",
      ">2, 216/468, d1=0.541, d2=0.609 g=0.846\n",
      ">2, 217/468, d1=0.551, d2=0.654 g=0.816\n",
      ">2, 218/468, d1=0.508, d2=0.607 g=0.848\n",
      ">2, 219/468, d1=0.506, d2=0.643 g=0.884\n",
      ">2, 220/468, d1=0.531, d2=0.615 g=0.918\n",
      ">2, 221/468, d1=0.570, d2=0.622 g=0.941\n",
      ">2, 222/468, d1=0.574, d2=0.577 g=0.932\n",
      ">2, 223/468, d1=0.563, d2=0.556 g=0.989\n",
      ">2, 224/468, d1=0.527, d2=0.531 g=1.015\n",
      ">2, 225/468, d1=0.545, d2=0.529 g=1.018\n",
      ">2, 226/468, d1=0.564, d2=0.548 g=1.056\n",
      ">2, 227/468, d1=0.540, d2=0.572 g=0.963\n",
      ">2, 228/468, d1=0.584, d2=0.585 g=0.916\n",
      ">2, 229/468, d1=0.560, d2=0.614 g=0.913\n",
      ">2, 230/468, d1=0.494, d2=0.662 g=0.870\n",
      ">2, 231/468, d1=0.532, d2=0.613 g=0.878\n",
      ">2, 232/468, d1=0.568, d2=0.590 g=0.916\n",
      ">2, 233/468, d1=0.547, d2=0.648 g=0.921\n",
      ">2, 234/468, d1=0.528, d2=0.626 g=0.930\n",
      ">2, 235/468, d1=0.431, d2=0.541 g=0.981\n",
      ">2, 236/468, d1=0.508, d2=0.547 g=0.982\n",
      ">2, 237/468, d1=0.558, d2=0.581 g=0.939\n",
      ">2, 238/468, d1=0.494, d2=0.604 g=0.912\n",
      ">2, 239/468, d1=0.520, d2=0.611 g=0.904\n",
      ">2, 240/468, d1=0.536, d2=0.619 g=0.879\n",
      ">2, 241/468, d1=0.572, d2=0.665 g=0.811\n",
      ">2, 242/468, d1=0.523, d2=0.644 g=0.852\n",
      ">2, 243/468, d1=0.565, d2=0.714 g=0.856\n",
      ">2, 244/468, d1=0.547, d2=0.609 g=0.931\n",
      ">2, 245/468, d1=0.538, d2=0.589 g=0.972\n",
      ">2, 246/468, d1=0.558, d2=0.578 g=1.000\n",
      ">2, 247/468, d1=0.525, d2=0.563 g=0.980\n",
      ">2, 248/468, d1=0.496, d2=0.583 g=0.947\n",
      ">2, 249/468, d1=0.511, d2=0.609 g=0.934\n",
      ">2, 250/468, d1=0.534, d2=0.582 g=0.918\n",
      ">2, 251/468, d1=0.557, d2=0.676 g=0.901\n",
      ">2, 252/468, d1=0.494, d2=0.650 g=0.932\n",
      ">2, 253/468, d1=0.553, d2=0.639 g=0.844\n",
      ">2, 254/468, d1=0.536, d2=0.680 g=0.853\n",
      ">2, 255/468, d1=0.524, d2=0.693 g=0.896\n",
      ">2, 256/468, d1=0.504, d2=0.586 g=0.923\n",
      ">2, 257/468, d1=0.500, d2=0.561 g=0.939\n",
      ">2, 258/468, d1=0.507, d2=0.513 g=0.965\n",
      ">2, 259/468, d1=0.512, d2=0.582 g=0.933\n",
      ">2, 260/468, d1=0.529, d2=0.572 g=0.874\n",
      ">2, 261/468, d1=0.490, d2=0.565 g=0.879\n",
      ">2, 262/468, d1=0.421, d2=0.601 g=0.885\n",
      ">2, 263/468, d1=0.514, d2=0.658 g=0.844\n",
      ">2, 264/468, d1=0.577, d2=0.624 g=0.903\n",
      ">2, 265/468, d1=0.581, d2=0.625 g=0.974\n",
      ">2, 266/468, d1=0.505, d2=0.537 g=0.997\n",
      ">2, 267/468, d1=0.502, d2=0.537 g=1.030\n",
      ">2, 268/468, d1=0.562, d2=0.484 g=0.987\n",
      ">2, 269/468, d1=0.519, d2=0.539 g=1.037\n",
      ">2, 270/468, d1=0.498, d2=0.550 g=1.000\n",
      ">2, 271/468, d1=0.478, d2=0.622 g=0.950\n",
      ">2, 272/468, d1=0.519, d2=0.624 g=0.906\n",
      ">2, 273/468, d1=0.453, d2=0.625 g=0.896\n",
      ">2, 274/468, d1=0.502, d2=0.645 g=0.896\n",
      ">2, 275/468, d1=0.525, d2=0.625 g=0.935\n",
      ">2, 276/468, d1=0.496, d2=0.584 g=0.961\n",
      ">2, 277/468, d1=0.431, d2=0.573 g=0.968\n",
      ">2, 278/468, d1=0.527, d2=0.586 g=0.983\n",
      ">2, 279/468, d1=0.483, d2=0.580 g=1.008\n",
      ">2, 280/468, d1=0.467, d2=0.617 g=0.966\n",
      ">2, 281/468, d1=0.485, d2=0.626 g=0.941\n",
      ">2, 282/468, d1=0.549, d2=0.616 g=0.981\n",
      ">2, 283/468, d1=0.513, d2=0.610 g=0.959\n",
      ">2, 284/468, d1=0.570, d2=0.516 g=1.061\n",
      ">2, 285/468, d1=0.602, d2=0.539 g=1.017\n",
      ">2, 286/468, d1=0.484, d2=0.568 g=1.078\n",
      ">2, 287/468, d1=0.591, d2=0.527 g=1.060\n",
      ">2, 288/468, d1=0.554, d2=0.531 g=1.027\n",
      ">2, 289/468, d1=0.521, d2=0.592 g=1.017\n",
      ">2, 290/468, d1=0.544, d2=0.577 g=0.935\n",
      ">2, 291/468, d1=0.510, d2=0.620 g=0.896\n",
      ">2, 292/468, d1=0.544, d2=0.643 g=0.941\n",
      ">2, 293/468, d1=0.514, d2=0.671 g=0.936\n",
      ">2, 294/468, d1=0.484, d2=0.593 g=0.957\n",
      ">2, 295/468, d1=0.519, d2=0.627 g=0.935\n",
      ">2, 296/468, d1=0.548, d2=0.636 g=0.931\n",
      ">2, 297/468, d1=0.538, d2=0.634 g=0.913\n",
      ">2, 298/468, d1=0.474, d2=0.692 g=0.863\n",
      ">2, 299/468, d1=0.549, d2=0.685 g=0.869\n",
      ">2, 300/468, d1=0.582, d2=0.690 g=0.932\n",
      ">2, 301/468, d1=0.596, d2=0.635 g=0.933\n",
      ">2, 302/468, d1=0.624, d2=0.647 g=0.884\n",
      ">2, 303/468, d1=0.604, d2=0.631 g=0.887\n",
      ">2, 304/468, d1=0.579, d2=0.652 g=0.845\n",
      ">2, 305/468, d1=0.601, d2=0.692 g=0.831\n",
      ">2, 306/468, d1=0.647, d2=0.752 g=0.766\n",
      ">2, 307/468, d1=0.599, d2=0.737 g=0.819\n",
      ">2, 308/468, d1=0.608, d2=0.702 g=0.796\n",
      ">2, 309/468, d1=0.661, d2=0.738 g=0.799\n",
      ">2, 310/468, d1=0.678, d2=0.787 g=0.804\n",
      ">2, 311/468, d1=0.686, d2=0.765 g=0.781\n",
      ">2, 312/468, d1=0.623, d2=0.778 g=0.826\n",
      ">2, 313/468, d1=0.665, d2=0.743 g=0.826\n",
      ">2, 314/468, d1=0.778, d2=0.745 g=0.792\n",
      ">2, 315/468, d1=0.706, d2=0.744 g=0.816\n",
      ">2, 316/468, d1=0.696, d2=0.711 g=0.835\n",
      ">2, 317/468, d1=0.734, d2=0.729 g=0.746\n",
      ">2, 318/468, d1=0.727, d2=0.732 g=0.774\n",
      ">2, 319/468, d1=0.730, d2=0.739 g=0.752\n",
      ">2, 320/468, d1=0.696, d2=0.779 g=0.764\n",
      ">2, 321/468, d1=0.693, d2=0.722 g=0.770\n",
      ">2, 322/468, d1=0.692, d2=0.768 g=0.767\n",
      ">2, 323/468, d1=0.702, d2=0.733 g=0.755\n",
      ">2, 324/468, d1=0.777, d2=0.733 g=0.789\n",
      ">2, 325/468, d1=0.695, d2=0.747 g=0.777\n",
      ">2, 326/468, d1=0.697, d2=0.706 g=0.760\n",
      ">2, 327/468, d1=0.733, d2=0.704 g=0.737\n",
      ">2, 328/468, d1=0.713, d2=0.736 g=0.757\n",
      ">2, 329/468, d1=0.628, d2=0.732 g=0.734\n",
      ">2, 330/468, d1=0.721, d2=0.761 g=0.708\n",
      ">2, 331/468, d1=0.695, d2=0.751 g=0.761\n",
      ">2, 332/468, d1=0.718, d2=0.710 g=0.795\n",
      ">2, 333/468, d1=0.672, d2=0.687 g=0.806\n",
      ">2, 334/468, d1=0.715, d2=0.703 g=0.757\n",
      ">2, 335/468, d1=0.701, d2=0.717 g=0.761\n",
      ">2, 336/468, d1=0.693, d2=0.703 g=0.744\n",
      ">2, 337/468, d1=0.656, d2=0.712 g=0.757\n",
      ">2, 338/468, d1=0.706, d2=0.719 g=0.752\n",
      ">2, 339/468, d1=0.631, d2=0.746 g=0.737\n",
      ">2, 340/468, d1=0.657, d2=0.733 g=0.752\n",
      ">2, 341/468, d1=0.660, d2=0.739 g=0.760\n",
      ">2, 342/468, d1=0.688, d2=0.711 g=0.774\n",
      ">2, 343/468, d1=0.662, d2=0.713 g=0.773\n",
      ">2, 344/468, d1=0.645, d2=0.706 g=0.775\n",
      ">2, 345/468, d1=0.699, d2=0.712 g=0.770\n",
      ">2, 346/468, d1=0.701, d2=0.736 g=0.769\n",
      ">2, 347/468, d1=0.693, d2=0.734 g=0.747\n",
      ">2, 348/468, d1=0.672, d2=0.753 g=0.768\n",
      ">2, 349/468, d1=0.668, d2=0.711 g=0.740\n",
      ">2, 350/468, d1=0.653, d2=0.744 g=0.759\n",
      ">2, 351/468, d1=0.669, d2=0.731 g=0.748\n",
      ">2, 352/468, d1=0.668, d2=0.720 g=0.761\n",
      ">2, 353/468, d1=0.713, d2=0.709 g=0.762\n",
      ">2, 354/468, d1=0.702, d2=0.693 g=0.760\n",
      ">2, 355/468, d1=0.728, d2=0.728 g=0.765\n",
      ">2, 356/468, d1=0.708, d2=0.718 g=0.739\n",
      ">2, 357/468, d1=0.706, d2=0.711 g=0.728\n",
      ">2, 358/468, d1=0.716, d2=0.759 g=0.743\n",
      ">2, 359/468, d1=0.702, d2=0.722 g=0.767\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">2, 360/468, d1=0.693, d2=0.743 g=0.766\n",
      ">2, 361/468, d1=0.725, d2=0.698 g=0.760\n",
      ">2, 362/468, d1=0.687, d2=0.707 g=0.775\n",
      ">2, 363/468, d1=0.708, d2=0.681 g=0.791\n",
      ">2, 364/468, d1=0.695, d2=0.707 g=0.780\n",
      ">2, 365/468, d1=0.709, d2=0.730 g=0.769\n",
      ">2, 366/468, d1=0.733, d2=0.717 g=0.742\n",
      ">2, 367/468, d1=0.711, d2=0.720 g=0.739\n",
      ">2, 368/468, d1=0.702, d2=0.751 g=0.745\n",
      ">2, 369/468, d1=0.724, d2=0.724 g=0.792\n",
      ">2, 370/468, d1=0.705, d2=0.706 g=0.792\n",
      ">2, 371/468, d1=0.724, d2=0.677 g=0.803\n",
      ">2, 372/468, d1=0.739, d2=0.630 g=0.839\n",
      ">2, 373/468, d1=0.696, d2=0.630 g=0.854\n",
      ">2, 374/468, d1=0.723, d2=0.639 g=0.853\n",
      ">2, 375/468, d1=0.747, d2=0.693 g=0.806\n",
      ">2, 376/468, d1=0.702, d2=0.721 g=0.775\n",
      ">2, 377/468, d1=0.711, d2=0.719 g=0.765\n",
      ">2, 378/468, d1=0.712, d2=0.710 g=0.766\n",
      ">2, 379/468, d1=0.734, d2=0.690 g=0.770\n",
      ">2, 380/468, d1=0.691, d2=0.676 g=0.793\n",
      ">2, 381/468, d1=0.699, d2=0.668 g=0.814\n",
      ">2, 382/468, d1=0.685, d2=0.679 g=0.823\n",
      ">2, 383/468, d1=0.692, d2=0.662 g=0.820\n",
      ">2, 384/468, d1=0.661, d2=0.653 g=0.797\n",
      ">2, 385/468, d1=0.655, d2=0.671 g=0.785\n",
      ">2, 386/468, d1=0.702, d2=0.706 g=0.759\n",
      ">2, 387/468, d1=0.672, d2=0.746 g=0.759\n",
      ">2, 388/468, d1=0.737, d2=0.717 g=0.755\n",
      ">2, 389/468, d1=0.697, d2=0.744 g=0.760\n",
      ">2, 390/468, d1=0.707, d2=0.735 g=0.772\n",
      ">2, 391/468, d1=0.730, d2=0.689 g=0.824\n",
      ">2, 392/468, d1=0.747, d2=0.637 g=0.845\n",
      ">2, 393/468, d1=0.747, d2=0.630 g=0.863\n",
      ">2, 394/468, d1=0.690, d2=0.638 g=0.879\n",
      ">2, 395/468, d1=0.714, d2=0.625 g=0.875\n",
      ">2, 396/468, d1=0.719, d2=0.668 g=0.842\n",
      ">2, 397/468, d1=0.734, d2=0.627 g=0.813\n",
      ">2, 398/468, d1=0.693, d2=0.656 g=0.798\n",
      ">2, 399/468, d1=0.738, d2=0.662 g=0.777\n",
      ">2, 400/468, d1=0.696, d2=0.689 g=0.767\n",
      ">2, 401/468, d1=0.703, d2=0.713 g=0.743\n",
      ">2, 402/468, d1=0.689, d2=0.731 g=0.747\n",
      ">2, 403/468, d1=0.711, d2=0.717 g=0.750\n",
      ">2, 404/468, d1=0.682, d2=0.708 g=0.749\n",
      ">2, 405/468, d1=0.713, d2=0.712 g=0.761\n",
      ">2, 406/468, d1=0.689, d2=0.729 g=0.748\n",
      ">2, 407/468, d1=0.714, d2=0.698 g=0.763\n",
      ">2, 408/468, d1=0.717, d2=0.695 g=0.751\n",
      ">2, 409/468, d1=0.706, d2=0.696 g=0.763\n",
      ">2, 410/468, d1=0.688, d2=0.705 g=0.773\n",
      ">2, 411/468, d1=0.720, d2=0.678 g=0.775\n",
      ">2, 412/468, d1=0.673, d2=0.725 g=0.779\n",
      ">2, 413/468, d1=0.718, d2=0.669 g=0.772\n",
      ">2, 414/468, d1=0.712, d2=0.703 g=0.762\n",
      ">2, 415/468, d1=0.694, d2=0.700 g=0.781\n",
      ">2, 416/468, d1=0.711, d2=0.697 g=0.789\n",
      ">2, 417/468, d1=0.714, d2=0.677 g=0.774\n",
      ">2, 418/468, d1=0.688, d2=0.717 g=0.777\n",
      ">2, 419/468, d1=0.731, d2=0.705 g=0.774\n",
      ">2, 420/468, d1=0.701, d2=0.679 g=0.824\n",
      ">2, 421/468, d1=0.707, d2=0.651 g=0.826\n",
      ">2, 422/468, d1=0.737, d2=0.657 g=0.812\n",
      ">2, 423/468, d1=0.715, d2=0.626 g=0.821\n",
      ">2, 424/468, d1=0.723, d2=0.666 g=0.837\n",
      ">2, 425/468, d1=0.698, d2=0.643 g=0.855\n",
      ">2, 426/468, d1=0.715, d2=0.633 g=0.858\n",
      ">2, 427/468, d1=0.730, d2=0.618 g=0.855\n",
      ">2, 428/468, d1=0.701, d2=0.590 g=0.883\n",
      ">2, 429/468, d1=0.733, d2=0.642 g=0.865\n",
      ">2, 430/468, d1=0.717, d2=0.619 g=0.844\n",
      ">2, 431/468, d1=0.696, d2=0.623 g=0.819\n",
      ">2, 432/468, d1=0.683, d2=0.641 g=0.803\n",
      ">2, 433/468, d1=0.661, d2=0.658 g=0.796\n",
      ">2, 434/468, d1=0.660, d2=0.676 g=0.769\n",
      ">2, 435/468, d1=0.694, d2=0.695 g=0.777\n",
      ">2, 436/468, d1=0.696, d2=0.703 g=0.767\n",
      ">2, 437/468, d1=0.664, d2=0.705 g=0.780\n",
      ">2, 438/468, d1=0.691, d2=0.682 g=0.747\n",
      ">2, 439/468, d1=0.713, d2=0.683 g=0.770\n",
      ">2, 440/468, d1=0.697, d2=0.676 g=0.764\n",
      ">2, 441/468, d1=0.700, d2=0.685 g=0.788\n",
      ">2, 442/468, d1=0.676, d2=0.666 g=0.782\n",
      ">2, 443/468, d1=0.668, d2=0.664 g=0.782\n",
      ">2, 444/468, d1=0.666, d2=0.655 g=0.762\n",
      ">2, 445/468, d1=0.681, d2=0.694 g=0.780\n",
      ">2, 446/468, d1=0.667, d2=0.679 g=0.766\n",
      ">2, 447/468, d1=0.681, d2=0.675 g=0.760\n",
      ">2, 448/468, d1=0.687, d2=0.690 g=0.767\n",
      ">2, 449/468, d1=0.673, d2=0.686 g=0.755\n",
      ">2, 450/468, d1=0.631, d2=0.699 g=0.745\n",
      ">2, 451/468, d1=0.685, d2=0.711 g=0.763\n",
      ">2, 452/468, d1=0.697, d2=0.694 g=0.775\n",
      ">2, 453/468, d1=0.675, d2=0.661 g=0.791\n",
      ">2, 454/468, d1=0.651, d2=0.659 g=0.795\n",
      ">2, 455/468, d1=0.687, d2=0.653 g=0.800\n",
      ">2, 456/468, d1=0.706, d2=0.652 g=0.818\n",
      ">2, 457/468, d1=0.711, d2=0.638 g=0.833\n",
      ">2, 458/468, d1=0.685, d2=0.651 g=0.838\n",
      ">2, 459/468, d1=0.718, d2=0.619 g=0.844\n",
      ">2, 460/468, d1=0.688, d2=0.636 g=0.838\n",
      ">2, 461/468, d1=0.682, d2=0.632 g=0.824\n",
      ">2, 462/468, d1=0.681, d2=0.666 g=0.798\n",
      ">2, 463/468, d1=0.685, d2=0.679 g=0.788\n",
      ">2, 464/468, d1=0.697, d2=0.687 g=0.792\n",
      ">2, 465/468, d1=0.714, d2=0.684 g=0.762\n",
      ">2, 466/468, d1=0.668, d2=0.686 g=0.768\n",
      ">2, 467/468, d1=0.690, d2=0.694 g=0.765\n",
      ">2, 468/468, d1=0.733, d2=0.667 g=0.749\n",
      ">3, 1/468, d1=0.695, d2=0.687 g=0.741\n",
      ">3, 2/468, d1=0.698, d2=0.704 g=0.759\n",
      ">3, 3/468, d1=0.685, d2=0.680 g=0.762\n",
      ">3, 4/468, d1=0.697, d2=0.686 g=0.746\n",
      ">3, 5/468, d1=0.691, d2=0.697 g=0.766\n",
      ">3, 6/468, d1=0.649, d2=0.660 g=0.766\n",
      ">3, 7/468, d1=0.670, d2=0.655 g=0.787\n",
      ">3, 8/468, d1=0.679, d2=0.679 g=0.792\n",
      ">3, 9/468, d1=0.695, d2=0.656 g=0.766\n",
      ">3, 10/468, d1=0.643, d2=0.699 g=0.755\n",
      ">3, 11/468, d1=0.672, d2=0.691 g=0.772\n",
      ">3, 12/468, d1=0.661, d2=0.679 g=0.766\n",
      ">3, 13/468, d1=0.678, d2=0.690 g=0.741\n",
      ">3, 14/468, d1=0.676, d2=0.718 g=0.751\n",
      ">3, 15/468, d1=0.662, d2=0.698 g=0.743\n",
      ">3, 16/468, d1=0.664, d2=0.697 g=0.740\n",
      ">3, 17/468, d1=0.723, d2=0.699 g=0.743\n",
      ">3, 18/468, d1=0.692, d2=0.701 g=0.763\n",
      ">3, 19/468, d1=0.684, d2=0.703 g=0.776\n",
      ">3, 20/468, d1=0.687, d2=0.679 g=0.781\n",
      ">3, 21/468, d1=0.697, d2=0.668 g=0.787\n",
      ">3, 22/468, d1=0.709, d2=0.666 g=0.785\n",
      ">3, 23/468, d1=0.691, d2=0.644 g=0.772\n",
      ">3, 24/468, d1=0.681, d2=0.660 g=0.797\n",
      ">3, 25/468, d1=0.715, d2=0.663 g=0.772\n",
      ">3, 26/468, d1=0.695, d2=0.657 g=0.786\n",
      ">3, 27/468, d1=0.676, d2=0.665 g=0.754\n",
      ">3, 28/468, d1=0.677, d2=0.677 g=0.760\n",
      ">3, 29/468, d1=0.677, d2=0.720 g=0.752\n",
      ">3, 30/468, d1=0.700, d2=0.707 g=0.740\n",
      ">3, 31/468, d1=0.687, d2=0.680 g=0.755\n",
      ">3, 32/468, d1=0.677, d2=0.686 g=0.751\n",
      ">3, 33/468, d1=0.690, d2=0.708 g=0.766\n",
      ">3, 34/468, d1=0.701, d2=0.690 g=0.753\n",
      ">3, 35/468, d1=0.676, d2=0.669 g=0.775\n",
      ">3, 36/468, d1=0.691, d2=0.659 g=0.785\n",
      ">3, 37/468, d1=0.701, d2=0.663 g=0.773\n",
      ">3, 38/468, d1=0.667, d2=0.659 g=0.765\n",
      ">3, 39/468, d1=0.672, d2=0.696 g=0.781\n",
      ">3, 40/468, d1=0.682, d2=0.653 g=0.779\n",
      ">3, 41/468, d1=0.641, d2=0.687 g=0.789\n",
      ">3, 42/468, d1=0.656, d2=0.660 g=0.756\n",
      ">3, 43/468, d1=0.670, d2=0.670 g=0.786\n",
      ">3, 44/468, d1=0.712, d2=0.695 g=0.746\n",
      ">3, 45/468, d1=0.638, d2=0.695 g=0.752\n",
      ">3, 46/468, d1=0.667, d2=0.740 g=0.740\n",
      ">3, 47/468, d1=0.652, d2=0.743 g=0.732\n",
      ">3, 48/468, d1=0.675, d2=0.738 g=0.723\n",
      ">3, 49/468, d1=0.652, d2=0.705 g=0.754\n",
      ">3, 50/468, d1=0.668, d2=0.682 g=0.753\n",
      ">3, 51/468, d1=0.670, d2=0.688 g=0.762\n",
      ">3, 52/468, d1=0.654, d2=0.687 g=0.761\n",
      ">3, 53/468, d1=0.673, d2=0.674 g=0.777\n",
      ">3, 54/468, d1=0.716, d2=0.682 g=0.724\n",
      ">3, 55/468, d1=0.701, d2=0.709 g=0.714\n",
      ">3, 56/468, d1=0.695, d2=0.748 g=0.731\n",
      ">3, 57/468, d1=0.686, d2=0.702 g=0.759\n",
      ">3, 58/468, d1=0.715, d2=0.705 g=0.748\n",
      ">3, 59/468, d1=0.713, d2=0.692 g=0.771\n",
      ">3, 60/468, d1=0.690, d2=0.672 g=0.762\n",
      ">3, 61/468, d1=0.680, d2=0.681 g=0.771\n",
      ">3, 62/468, d1=0.706, d2=0.699 g=0.753\n",
      ">3, 63/468, d1=0.662, d2=0.703 g=0.724\n",
      ">3, 64/468, d1=0.687, d2=0.755 g=0.696\n",
      ">3, 65/468, d1=0.674, d2=0.727 g=0.720\n",
      ">3, 66/468, d1=0.684, d2=0.726 g=0.727\n",
      ">3, 67/468, d1=0.702, d2=0.705 g=0.745\n",
      ">3, 68/468, d1=0.712, d2=0.665 g=0.778\n",
      ">3, 69/468, d1=0.740, d2=0.676 g=0.786\n",
      ">3, 70/468, d1=0.673, d2=0.673 g=0.775\n",
      ">3, 71/468, d1=0.728, d2=0.689 g=0.760\n",
      ">3, 72/468, d1=0.688, d2=0.718 g=0.745\n",
      ">3, 73/468, d1=0.704, d2=0.704 g=0.734\n",
      ">3, 74/468, d1=0.706, d2=0.715 g=0.716\n",
      ">3, 75/468, d1=0.709, d2=0.709 g=0.744\n",
      ">3, 76/468, d1=0.696, d2=0.689 g=0.751\n",
      ">3, 77/468, d1=0.691, d2=0.700 g=0.777\n",
      ">3, 78/468, d1=0.689, d2=0.668 g=0.784\n",
      ">3, 79/468, d1=0.666, d2=0.667 g=0.801\n",
      ">3, 80/468, d1=0.673, d2=0.666 g=0.770\n",
      ">3, 81/468, d1=0.665, d2=0.685 g=0.757\n",
      ">3, 82/468, d1=0.649, d2=0.694 g=0.739\n",
      ">3, 83/468, d1=0.667, d2=0.701 g=0.760\n",
      ">3, 84/468, d1=0.660, d2=0.676 g=0.795\n",
      ">3, 85/468, d1=0.687, d2=0.654 g=0.805\n",
      ">3, 86/468, d1=0.669, d2=0.621 g=0.828\n",
      ">3, 87/468, d1=0.696, d2=0.613 g=0.875\n",
      ">3, 88/468, d1=0.654, d2=0.589 g=0.881\n",
      ">3, 89/468, d1=0.649, d2=0.610 g=0.869\n",
      ">3, 90/468, d1=0.673, d2=0.627 g=0.814\n",
      ">3, 91/468, d1=0.660, d2=0.654 g=0.791\n",
      ">3, 92/468, d1=0.684, d2=0.695 g=0.746\n",
      ">3, 93/468, d1=0.673, d2=0.759 g=0.709\n",
      ">3, 94/468, d1=0.619, d2=0.749 g=0.745\n",
      ">3, 95/468, d1=0.650, d2=0.679 g=0.802\n",
      ">3, 96/468, d1=0.669, d2=0.610 g=0.865\n",
      ">3, 97/468, d1=0.677, d2=0.624 g=0.834\n",
      ">3, 98/468, d1=0.632, d2=0.633 g=0.833\n",
      ">3, 99/468, d1=0.656, d2=0.646 g=0.800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">3, 100/468, d1=0.678, d2=0.714 g=0.706\n",
      ">3, 101/468, d1=0.657, d2=0.786 g=0.694\n",
      ">3, 102/468, d1=0.633, d2=0.785 g=0.696\n",
      ">3, 103/468, d1=0.700, d2=0.782 g=0.710\n",
      ">3, 104/468, d1=0.673, d2=0.693 g=0.757\n",
      ">3, 105/468, d1=0.686, d2=0.640 g=0.848\n",
      ">3, 106/468, d1=0.680, d2=0.597 g=0.896\n",
      ">3, 107/468, d1=0.701, d2=0.575 g=0.900\n",
      ">3, 108/468, d1=0.665, d2=0.597 g=0.881\n",
      ">3, 109/468, d1=0.675, d2=0.646 g=0.838\n",
      ">3, 110/468, d1=0.685, d2=0.717 g=0.751\n",
      ">3, 111/468, d1=0.679, d2=0.825 g=0.661\n",
      ">3, 112/468, d1=0.678, d2=0.875 g=0.634\n",
      ">3, 113/468, d1=0.674, d2=0.806 g=0.703\n",
      ">3, 114/468, d1=0.700, d2=0.704 g=0.777\n",
      ">3, 115/468, d1=0.702, d2=0.634 g=0.861\n",
      ">3, 116/468, d1=0.668, d2=0.626 g=0.871\n",
      ">3, 117/468, d1=0.664, d2=0.618 g=0.846\n",
      ">3, 118/468, d1=0.691, d2=0.687 g=0.772\n",
      ">3, 119/468, d1=0.674, d2=0.754 g=0.728\n",
      ">3, 120/468, d1=0.691, d2=0.797 g=0.673\n",
      ">3, 121/468, d1=0.687, d2=0.770 g=0.684\n",
      ">3, 122/468, d1=0.689, d2=0.739 g=0.722\n",
      ">3, 123/468, d1=0.673, d2=0.706 g=0.765\n",
      ">3, 124/468, d1=0.690, d2=0.648 g=0.804\n",
      ">3, 125/468, d1=0.708, d2=0.602 g=0.835\n",
      ">3, 126/468, d1=0.661, d2=0.602 g=0.875\n",
      ">3, 127/468, d1=0.666, d2=0.584 g=0.869\n",
      ">3, 128/468, d1=0.676, d2=0.614 g=0.835\n",
      ">3, 129/468, d1=0.677, d2=0.664 g=0.777\n",
      ">3, 130/468, d1=0.661, d2=0.680 g=0.735\n",
      ">3, 131/468, d1=0.643, d2=0.730 g=0.707\n",
      ">3, 132/468, d1=0.674, d2=0.745 g=0.727\n",
      ">3, 133/468, d1=0.702, d2=0.708 g=0.770\n",
      ">3, 134/468, d1=0.663, d2=0.672 g=0.814\n",
      ">3, 135/468, d1=0.695, d2=0.623 g=0.836\n",
      ">3, 136/468, d1=0.666, d2=0.600 g=0.828\n",
      ">3, 137/468, d1=0.662, d2=0.645 g=0.814\n",
      ">3, 138/468, d1=0.638, d2=0.669 g=0.782\n",
      ">3, 139/468, d1=0.682, d2=0.731 g=0.736\n",
      ">3, 140/468, d1=0.673, d2=0.749 g=0.724\n",
      ">3, 141/468, d1=0.675, d2=0.720 g=0.721\n",
      ">3, 142/468, d1=0.727, d2=0.735 g=0.725\n",
      ">3, 143/468, d1=0.694, d2=0.689 g=0.752\n",
      ">3, 144/468, d1=0.677, d2=0.669 g=0.759\n",
      ">3, 145/468, d1=0.690, d2=0.658 g=0.785\n",
      ">3, 146/468, d1=0.677, d2=0.643 g=0.778\n",
      ">3, 147/468, d1=0.676, d2=0.664 g=0.778\n",
      ">3, 148/468, d1=0.685, d2=0.707 g=0.755\n",
      ">3, 149/468, d1=0.670, d2=0.690 g=0.753\n",
      ">3, 150/468, d1=0.709, d2=0.704 g=0.739\n",
      ">3, 151/468, d1=0.664, d2=0.695 g=0.762\n",
      ">3, 152/468, d1=0.650, d2=0.673 g=0.774\n",
      ">3, 153/468, d1=0.678, d2=0.687 g=0.784\n",
      ">3, 154/468, d1=0.683, d2=0.671 g=0.803\n",
      ">3, 155/468, d1=0.717, d2=0.638 g=0.826\n",
      ">3, 156/468, d1=0.709, d2=0.643 g=0.829\n",
      ">3, 157/468, d1=0.683, d2=0.617 g=0.809\n",
      ">3, 158/468, d1=0.704, d2=0.622 g=0.818\n",
      ">3, 159/468, d1=0.677, d2=0.657 g=0.767\n",
      ">3, 160/468, d1=0.699, d2=0.662 g=0.784\n",
      ">3, 161/468, d1=0.690, d2=0.717 g=0.725\n",
      ">3, 162/468, d1=0.704, d2=0.726 g=0.716\n",
      ">3, 163/468, d1=0.724, d2=0.715 g=0.720\n",
      ">3, 164/468, d1=0.705, d2=0.697 g=0.732\n",
      ">3, 165/468, d1=0.693, d2=0.689 g=0.748\n",
      ">3, 166/468, d1=0.677, d2=0.668 g=0.773\n",
      ">3, 167/468, d1=0.676, d2=0.660 g=0.784\n",
      ">3, 168/468, d1=0.685, d2=0.655 g=0.783\n",
      ">3, 169/468, d1=0.670, d2=0.650 g=0.783\n",
      ">3, 170/468, d1=0.673, d2=0.671 g=0.767\n",
      ">3, 171/468, d1=0.650, d2=0.732 g=0.741\n",
      ">3, 172/468, d1=0.690, d2=0.710 g=0.720\n",
      ">3, 173/468, d1=0.663, d2=0.742 g=0.712\n",
      ">3, 174/468, d1=0.677, d2=0.726 g=0.705\n",
      ">3, 175/468, d1=0.709, d2=0.739 g=0.749\n",
      ">3, 176/468, d1=0.694, d2=0.683 g=0.764\n",
      ">3, 177/468, d1=0.696, d2=0.654 g=0.809\n",
      ">3, 178/468, d1=0.709, d2=0.621 g=0.832\n",
      ">3, 179/468, d1=0.713, d2=0.623 g=0.855\n",
      ">3, 180/468, d1=0.715, d2=0.622 g=0.819\n",
      ">3, 181/468, d1=0.693, d2=0.633 g=0.821\n",
      ">3, 182/468, d1=0.697, d2=0.620 g=0.783\n",
      ">3, 183/468, d1=0.650, d2=0.645 g=0.758\n",
      ">3, 184/468, d1=0.685, d2=0.744 g=0.727\n",
      ">3, 185/468, d1=0.652, d2=0.720 g=0.698\n",
      ">3, 186/468, d1=0.681, d2=0.713 g=0.716\n",
      ">3, 187/468, d1=0.680, d2=0.731 g=0.718\n",
      ">3, 188/468, d1=0.655, d2=0.682 g=0.751\n",
      ">3, 189/468, d1=0.650, d2=0.663 g=0.794\n",
      ">3, 190/468, d1=0.675, d2=0.644 g=0.810\n",
      ">3, 191/468, d1=0.701, d2=0.594 g=0.854\n",
      ">3, 192/468, d1=0.676, d2=0.592 g=0.868\n",
      ">3, 193/468, d1=0.663, d2=0.590 g=0.889\n",
      ">3, 194/468, d1=0.673, d2=0.628 g=0.836\n",
      ">3, 195/468, d1=0.632, d2=0.667 g=0.779\n",
      ">3, 196/468, d1=0.597, d2=0.751 g=0.742\n",
      ">3, 197/468, d1=0.659, d2=0.796 g=0.698\n",
      ">3, 198/468, d1=0.683, d2=0.788 g=0.708\n",
      ">3, 199/468, d1=0.720, d2=0.751 g=0.750\n",
      ">3, 200/468, d1=0.706, d2=0.677 g=0.801\n",
      ">3, 201/468, d1=0.699, d2=0.628 g=0.858\n",
      ">3, 202/468, d1=0.739, d2=0.581 g=0.901\n",
      ">3, 203/468, d1=0.698, d2=0.559 g=0.970\n",
      ">3, 204/468, d1=0.731, d2=0.558 g=0.942\n",
      ">3, 205/468, d1=0.736, d2=0.585 g=0.888\n",
      ">3, 206/468, d1=0.665, d2=0.642 g=0.824\n",
      ">3, 207/468, d1=0.660, d2=0.675 g=0.736\n",
      ">3, 208/468, d1=0.698, d2=0.697 g=0.732\n",
      ">3, 209/468, d1=0.710, d2=0.723 g=0.689\n",
      ">3, 210/468, d1=0.659, d2=0.758 g=0.686\n",
      ">3, 211/468, d1=0.682, d2=0.750 g=0.710\n",
      ">3, 212/468, d1=0.665, d2=0.702 g=0.763\n",
      ">3, 213/468, d1=0.690, d2=0.647 g=0.798\n",
      ">3, 214/468, d1=0.650, d2=0.622 g=0.849\n",
      ">3, 215/468, d1=0.665, d2=0.584 g=0.867\n",
      ">3, 216/468, d1=0.684, d2=0.569 g=0.871\n",
      ">3, 217/468, d1=0.680, d2=0.575 g=0.861\n",
      ">3, 218/468, d1=0.654, d2=0.605 g=0.819\n",
      ">3, 219/468, d1=0.665, d2=0.701 g=0.755\n",
      ">3, 220/468, d1=0.622, d2=0.755 g=0.704\n",
      ">3, 221/468, d1=0.661, d2=0.762 g=0.682\n",
      ">3, 222/468, d1=0.654, d2=0.803 g=0.656\n",
      ">3, 223/468, d1=0.699, d2=0.845 g=0.703\n",
      ">3, 224/468, d1=0.684, d2=0.714 g=0.764\n",
      ">3, 225/468, d1=0.703, d2=0.642 g=0.834\n",
      ">3, 226/468, d1=0.733, d2=0.610 g=0.880\n",
      ">3, 227/468, d1=0.704, d2=0.604 g=0.881\n",
      ">3, 228/468, d1=0.713, d2=0.587 g=0.865\n",
      ">3, 229/468, d1=0.708, d2=0.636 g=0.824\n",
      ">3, 230/468, d1=0.697, d2=0.651 g=0.795\n",
      ">3, 231/468, d1=0.697, d2=0.673 g=0.751\n",
      ">3, 232/468, d1=0.675, d2=0.698 g=0.718\n",
      ">3, 233/468, d1=0.679, d2=0.720 g=0.702\n",
      ">3, 234/468, d1=0.674, d2=0.745 g=0.720\n",
      ">3, 235/468, d1=0.672, d2=0.711 g=0.728\n",
      ">3, 236/468, d1=0.661, d2=0.689 g=0.743\n",
      ">3, 237/468, d1=0.682, d2=0.690 g=0.764\n",
      ">3, 238/468, d1=0.678, d2=0.673 g=0.774\n",
      ">3, 239/468, d1=0.682, d2=0.659 g=0.791\n",
      ">3, 240/468, d1=0.686, d2=0.649 g=0.787\n",
      ">3, 241/468, d1=0.659, d2=0.644 g=0.786\n",
      ">3, 242/468, d1=0.678, d2=0.629 g=0.781\n",
      ">3, 243/468, d1=0.699, d2=0.657 g=0.776\n",
      ">3, 244/468, d1=0.640, d2=0.656 g=0.754\n",
      ">3, 245/468, d1=0.658, d2=0.662 g=0.756\n",
      ">3, 246/468, d1=0.690, d2=0.681 g=0.738\n",
      ">3, 247/468, d1=0.670, d2=0.730 g=0.714\n",
      ">3, 248/468, d1=0.666, d2=0.707 g=0.740\n",
      ">3, 249/468, d1=0.660, d2=0.697 g=0.748\n",
      ">3, 250/468, d1=0.677, d2=0.684 g=0.759\n",
      ">3, 251/468, d1=0.689, d2=0.680 g=0.779\n",
      ">3, 252/468, d1=0.703, d2=0.639 g=0.796\n",
      ">3, 253/468, d1=0.691, d2=0.650 g=0.787\n",
      ">3, 254/468, d1=0.689, d2=0.661 g=0.780\n",
      ">3, 255/468, d1=0.694, d2=0.657 g=0.765\n",
      ">3, 256/468, d1=0.667, d2=0.670 g=0.763\n",
      ">3, 257/468, d1=0.675, d2=0.663 g=0.766\n",
      ">3, 258/468, d1=0.682, d2=0.684 g=0.765\n",
      ">3, 259/468, d1=0.704, d2=0.669 g=0.757\n",
      ">3, 260/468, d1=0.683, d2=0.690 g=0.760\n",
      ">3, 261/468, d1=0.671, d2=0.678 g=0.739\n",
      ">3, 262/468, d1=0.690, d2=0.668 g=0.734\n",
      ">3, 263/468, d1=0.687, d2=0.684 g=0.758\n",
      ">3, 264/468, d1=0.680, d2=0.663 g=0.770\n",
      ">3, 265/468, d1=0.699, d2=0.659 g=0.786\n",
      ">3, 266/468, d1=0.668, d2=0.659 g=0.768\n",
      ">3, 267/468, d1=0.675, d2=0.662 g=0.773\n",
      ">3, 268/468, d1=0.717, d2=0.697 g=0.771\n",
      ">3, 269/468, d1=0.695, d2=0.681 g=0.763\n",
      ">3, 270/468, d1=0.679, d2=0.665 g=0.756\n",
      ">3, 271/468, d1=0.687, d2=0.665 g=0.757\n",
      ">3, 272/468, d1=0.697, d2=0.693 g=0.761\n",
      ">3, 273/468, d1=0.681, d2=0.661 g=0.769\n",
      ">3, 274/468, d1=0.666, d2=0.667 g=0.770\n",
      ">3, 275/468, d1=0.702, d2=0.654 g=0.778\n",
      ">3, 276/468, d1=0.704, d2=0.665 g=0.779\n",
      ">3, 277/468, d1=0.693, d2=0.676 g=0.791\n",
      ">3, 278/468, d1=0.667, d2=0.628 g=0.780\n",
      ">3, 279/468, d1=0.686, d2=0.633 g=0.772\n",
      ">3, 280/468, d1=0.684, d2=0.639 g=0.788\n",
      ">3, 281/468, d1=0.698, d2=0.637 g=0.776\n",
      ">3, 282/468, d1=0.676, d2=0.647 g=0.786\n",
      ">3, 283/468, d1=0.691, d2=0.665 g=0.775\n",
      ">3, 284/468, d1=0.687, d2=0.662 g=0.782\n",
      ">3, 285/468, d1=0.690, d2=0.674 g=0.747\n",
      ">3, 286/468, d1=0.683, d2=0.673 g=0.745\n",
      ">3, 287/468, d1=0.698, d2=0.679 g=0.750\n",
      ">3, 288/468, d1=0.683, d2=0.680 g=0.743\n",
      ">3, 289/468, d1=0.689, d2=0.681 g=0.744\n",
      ">3, 290/468, d1=0.666, d2=0.683 g=0.735\n",
      ">3, 291/468, d1=0.666, d2=0.686 g=0.749\n",
      ">3, 292/468, d1=0.667, d2=0.663 g=0.750\n",
      ">3, 293/468, d1=0.692, d2=0.686 g=0.749\n",
      ">3, 294/468, d1=0.673, d2=0.687 g=0.744\n",
      ">3, 295/468, d1=0.665, d2=0.668 g=0.745\n",
      ">3, 296/468, d1=0.685, d2=0.660 g=0.758\n",
      ">3, 297/468, d1=0.669, d2=0.698 g=0.738\n",
      ">3, 298/468, d1=0.702, d2=0.667 g=0.747\n",
      ">3, 299/468, d1=0.667, d2=0.693 g=0.739\n",
      ">3, 300/468, d1=0.677, d2=0.674 g=0.750\n",
      ">3, 301/468, d1=0.672, d2=0.681 g=0.750\n",
      ">3, 302/468, d1=0.683, d2=0.656 g=0.760\n",
      ">3, 303/468, d1=0.689, d2=0.679 g=0.751\n",
      ">3, 304/468, d1=0.686, d2=0.663 g=0.746\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">3, 305/468, d1=0.681, d2=0.681 g=0.752\n",
      ">3, 306/468, d1=0.677, d2=0.679 g=0.752\n",
      ">3, 307/468, d1=0.663, d2=0.680 g=0.753\n",
      ">3, 308/468, d1=0.644, d2=0.657 g=0.741\n",
      ">3, 309/468, d1=0.670, d2=0.695 g=0.747\n",
      ">3, 310/468, d1=0.701, d2=0.682 g=0.734\n",
      ">3, 311/468, d1=0.665, d2=0.686 g=0.738\n",
      ">3, 312/468, d1=0.656, d2=0.677 g=0.749\n",
      ">3, 313/468, d1=0.671, d2=0.673 g=0.732\n",
      ">3, 314/468, d1=0.646, d2=0.690 g=0.738\n",
      ">3, 315/468, d1=0.681, d2=0.670 g=0.743\n",
      ">3, 316/468, d1=0.702, d2=0.670 g=0.746\n",
      ">3, 317/468, d1=0.672, d2=0.680 g=0.763\n",
      ">3, 318/468, d1=0.670, d2=0.673 g=0.773\n",
      ">3, 319/468, d1=0.686, d2=0.658 g=0.771\n",
      ">3, 320/468, d1=0.637, d2=0.658 g=0.772\n",
      ">3, 321/468, d1=0.668, d2=0.678 g=0.773\n",
      ">3, 322/468, d1=0.668, d2=0.680 g=0.760\n",
      ">3, 323/468, d1=0.636, d2=0.683 g=0.747\n",
      ">3, 324/468, d1=0.659, d2=0.684 g=0.756\n",
      ">3, 325/468, d1=0.657, d2=0.669 g=0.746\n",
      ">3, 326/468, d1=0.668, d2=0.678 g=0.765\n",
      ">3, 327/468, d1=0.668, d2=0.672 g=0.760\n",
      ">3, 328/468, d1=0.642, d2=0.670 g=0.772\n",
      ">3, 329/468, d1=0.663, d2=0.674 g=0.755\n",
      ">3, 330/468, d1=0.665, d2=0.670 g=0.772\n",
      ">3, 331/468, d1=0.661, d2=0.651 g=0.768\n",
      ">3, 332/468, d1=0.684, d2=0.671 g=0.771\n",
      ">3, 333/468, d1=0.633, d2=0.663 g=0.767\n",
      ">3, 334/468, d1=0.666, d2=0.664 g=0.759\n",
      ">3, 335/468, d1=0.647, d2=0.662 g=0.760\n",
      ">3, 336/468, d1=0.663, d2=0.680 g=0.747\n",
      ">3, 337/468, d1=0.658, d2=0.682 g=0.740\n",
      ">3, 338/468, d1=0.716, d2=0.672 g=0.735\n",
      ">3, 339/468, d1=0.693, d2=0.690 g=0.743\n",
      ">3, 340/468, d1=0.678, d2=0.671 g=0.745\n",
      ">3, 341/468, d1=0.698, d2=0.686 g=0.751\n",
      ">3, 342/468, d1=0.656, d2=0.673 g=0.751\n",
      ">3, 343/468, d1=0.655, d2=0.670 g=0.748\n",
      ">3, 344/468, d1=0.653, d2=0.657 g=0.769\n",
      ">3, 345/468, d1=0.656, d2=0.673 g=0.763\n",
      ">3, 346/468, d1=0.660, d2=0.671 g=0.769\n",
      ">3, 347/468, d1=0.681, d2=0.665 g=0.752\n",
      ">3, 348/468, d1=0.673, d2=0.706 g=0.743\n",
      ">3, 349/468, d1=0.667, d2=0.689 g=0.734\n",
      ">3, 350/468, d1=0.715, d2=0.706 g=0.725\n",
      ">3, 351/468, d1=0.673, d2=0.685 g=0.750\n",
      ">3, 352/468, d1=0.698, d2=0.704 g=0.750\n",
      ">3, 353/468, d1=0.688, d2=0.680 g=0.763\n",
      ">3, 354/468, d1=0.674, d2=0.665 g=0.777\n",
      ">3, 355/468, d1=0.699, d2=0.666 g=0.786\n",
      ">3, 356/468, d1=0.690, d2=0.643 g=0.801\n",
      ">3, 357/468, d1=0.704, d2=0.637 g=0.806\n",
      ">3, 358/468, d1=0.727, d2=0.626 g=0.783\n",
      ">3, 359/468, d1=0.698, d2=0.638 g=0.795\n",
      ">3, 360/468, d1=0.681, d2=0.662 g=0.764\n",
      ">3, 361/468, d1=0.688, d2=0.690 g=0.745\n",
      ">3, 362/468, d1=0.671, d2=0.670 g=0.757\n",
      ">3, 363/468, d1=0.677, d2=0.684 g=0.750\n",
      ">3, 364/468, d1=0.672, d2=0.694 g=0.751\n",
      ">3, 365/468, d1=0.706, d2=0.664 g=0.768\n",
      ">3, 366/468, d1=0.695, d2=0.657 g=0.771\n",
      ">3, 367/468, d1=0.702, d2=0.640 g=0.793\n",
      ">3, 368/468, d1=0.659, d2=0.628 g=0.803\n",
      ">3, 369/468, d1=0.666, d2=0.625 g=0.791\n",
      ">3, 370/468, d1=0.692, d2=0.649 g=0.806\n",
      ">3, 371/468, d1=0.651, d2=0.638 g=0.780\n",
      ">3, 372/468, d1=0.678, d2=0.674 g=0.766\n",
      ">3, 373/468, d1=0.650, d2=0.690 g=0.731\n",
      ">3, 374/468, d1=0.715, d2=0.718 g=0.749\n",
      ">3, 375/468, d1=0.653, d2=0.707 g=0.726\n",
      ">3, 376/468, d1=0.648, d2=0.725 g=0.732\n",
      ">3, 377/468, d1=0.697, d2=0.687 g=0.757\n",
      ">3, 378/468, d1=0.683, d2=0.649 g=0.794\n",
      ">3, 379/468, d1=0.707, d2=0.641 g=0.841\n",
      ">3, 380/468, d1=0.719, d2=0.623 g=0.844\n",
      ">3, 381/468, d1=0.700, d2=0.633 g=0.859\n",
      ">3, 382/468, d1=0.679, d2=0.619 g=0.846\n",
      ">3, 383/468, d1=0.713, d2=0.622 g=0.816\n",
      ">3, 384/468, d1=0.708, d2=0.635 g=0.807\n",
      ">3, 385/468, d1=0.702, d2=0.668 g=0.773\n",
      ">3, 386/468, d1=0.680, d2=0.691 g=0.744\n",
      ">3, 387/468, d1=0.668, d2=0.686 g=0.741\n",
      ">3, 388/468, d1=0.665, d2=0.691 g=0.750\n",
      ">3, 389/468, d1=0.678, d2=0.668 g=0.764\n",
      ">3, 390/468, d1=0.671, d2=0.689 g=0.779\n",
      ">3, 391/468, d1=0.697, d2=0.657 g=0.762\n",
      ">3, 392/468, d1=0.671, d2=0.621 g=0.793\n",
      ">3, 393/468, d1=0.632, d2=0.621 g=0.787\n",
      ">3, 394/468, d1=0.690, d2=0.629 g=0.808\n",
      ">3, 395/468, d1=0.674, d2=0.645 g=0.785\n",
      ">3, 396/468, d1=0.666, d2=0.661 g=0.750\n",
      ">3, 397/468, d1=0.671, d2=0.682 g=0.711\n",
      ">3, 398/468, d1=0.638, d2=0.733 g=0.729\n",
      ">3, 399/468, d1=0.654, d2=0.685 g=0.741\n",
      ">3, 400/468, d1=0.674, d2=0.687 g=0.744\n",
      ">3, 401/468, d1=0.659, d2=0.705 g=0.765\n",
      ">3, 402/468, d1=0.674, d2=0.640 g=0.788\n",
      ">3, 403/468, d1=0.704, d2=0.620 g=0.838\n",
      ">3, 404/468, d1=0.705, d2=0.631 g=0.844\n",
      ">3, 405/468, d1=0.692, d2=0.620 g=0.821\n",
      ">3, 406/468, d1=0.699, d2=0.612 g=0.835\n",
      ">3, 407/468, d1=0.722, d2=0.630 g=0.781\n",
      ">3, 408/468, d1=0.669, d2=0.650 g=0.774\n",
      ">3, 409/468, d1=0.692, d2=0.682 g=0.746\n",
      ">3, 410/468, d1=0.709, d2=0.684 g=0.739\n",
      ">3, 411/468, d1=0.674, d2=0.687 g=0.726\n",
      ">3, 412/468, d1=0.685, d2=0.706 g=0.737\n",
      ">3, 413/468, d1=0.685, d2=0.695 g=0.747\n",
      ">3, 414/468, d1=0.670, d2=0.668 g=0.767\n",
      ">3, 415/468, d1=0.667, d2=0.637 g=0.775\n",
      ">3, 416/468, d1=0.685, d2=0.637 g=0.777\n",
      ">3, 417/468, d1=0.676, d2=0.674 g=0.791\n",
      ">3, 418/468, d1=0.699, d2=0.649 g=0.780\n",
      ">3, 419/468, d1=0.643, d2=0.640 g=0.795\n",
      ">3, 420/468, d1=0.666, d2=0.645 g=0.749\n",
      ">3, 421/468, d1=0.646, d2=0.692 g=0.760\n",
      ">3, 422/468, d1=0.650, d2=0.683 g=0.746\n",
      ">3, 423/468, d1=0.657, d2=0.693 g=0.750\n",
      ">3, 424/468, d1=0.671, d2=0.697 g=0.753\n",
      ">3, 425/468, d1=0.675, d2=0.684 g=0.762\n",
      ">3, 426/468, d1=0.723, d2=0.672 g=0.781\n",
      ">3, 427/468, d1=0.683, d2=0.645 g=0.801\n",
      ">3, 428/468, d1=0.704, d2=0.634 g=0.819\n",
      ">3, 429/468, d1=0.691, d2=0.618 g=0.839\n",
      ">3, 430/468, d1=0.671, d2=0.628 g=0.841\n",
      ">3, 431/468, d1=0.669, d2=0.625 g=0.832\n",
      ">3, 432/468, d1=0.675, d2=0.612 g=0.834\n",
      ">3, 433/468, d1=0.675, d2=0.629 g=0.812\n",
      ">3, 434/468, d1=0.684, d2=0.645 g=0.784\n",
      ">3, 435/468, d1=0.709, d2=0.660 g=0.753\n",
      ">3, 436/468, d1=0.687, d2=0.703 g=0.728\n",
      ">3, 437/468, d1=0.670, d2=0.712 g=0.719\n",
      ">3, 438/468, d1=0.670, d2=0.701 g=0.729\n",
      ">3, 439/468, d1=0.679, d2=0.682 g=0.739\n",
      ">3, 440/468, d1=0.655, d2=0.679 g=0.763\n",
      ">3, 441/468, d1=0.654, d2=0.656 g=0.765\n",
      ">3, 442/468, d1=0.655, d2=0.644 g=0.787\n",
      ">3, 443/468, d1=0.672, d2=0.656 g=0.806\n",
      ">3, 444/468, d1=0.649, d2=0.632 g=0.800\n",
      ">3, 445/468, d1=0.662, d2=0.652 g=0.791\n",
      ">3, 446/468, d1=0.627, d2=0.664 g=0.784\n",
      ">3, 447/468, d1=0.670, d2=0.670 g=0.767\n",
      ">3, 448/468, d1=0.651, d2=0.682 g=0.753\n",
      ">3, 449/468, d1=0.655, d2=0.694 g=0.738\n",
      ">3, 450/468, d1=0.621, d2=0.702 g=0.730\n",
      ">3, 451/468, d1=0.687, d2=0.707 g=0.737\n",
      ">3, 452/468, d1=0.701, d2=0.688 g=0.753\n",
      ">3, 453/468, d1=0.681, d2=0.677 g=0.753\n",
      ">3, 454/468, d1=0.687, d2=0.654 g=0.788\n",
      ">3, 455/468, d1=0.683, d2=0.627 g=0.810\n",
      ">3, 456/468, d1=0.661, d2=0.636 g=0.814\n",
      ">3, 457/468, d1=0.664, d2=0.613 g=0.828\n",
      ">3, 458/468, d1=0.700, d2=0.621 g=0.805\n",
      ">3, 459/468, d1=0.677, d2=0.633 g=0.810\n",
      ">3, 460/468, d1=0.669, d2=0.652 g=0.781\n",
      ">3, 461/468, d1=0.681, d2=0.675 g=0.764\n",
      ">3, 462/468, d1=0.693, d2=0.691 g=0.743\n",
      ">3, 463/468, d1=0.653, d2=0.709 g=0.731\n",
      ">3, 464/468, d1=0.690, d2=0.672 g=0.724\n",
      ">3, 465/468, d1=0.653, d2=0.699 g=0.720\n",
      ">3, 466/468, d1=0.647, d2=0.679 g=0.745\n",
      ">3, 467/468, d1=0.665, d2=0.686 g=0.756\n",
      ">3, 468/468, d1=0.670, d2=0.671 g=0.755\n",
      ">4, 1/468, d1=0.692, d2=0.654 g=0.787\n",
      ">4, 2/468, d1=0.651, d2=0.639 g=0.797\n",
      ">4, 3/468, d1=0.623, d2=0.644 g=0.781\n",
      ">4, 4/468, d1=0.659, d2=0.661 g=0.767\n",
      ">4, 5/468, d1=0.647, d2=0.672 g=0.779\n",
      ">4, 6/468, d1=0.652, d2=0.683 g=0.759\n",
      ">4, 7/468, d1=0.672, d2=0.667 g=0.765\n",
      ">4, 8/468, d1=0.667, d2=0.681 g=0.746\n",
      ">4, 9/468, d1=0.684, d2=0.699 g=0.750\n",
      ">4, 10/468, d1=0.695, d2=0.688 g=0.745\n",
      ">4, 11/468, d1=0.647, d2=0.679 g=0.775\n",
      ">4, 12/468, d1=0.680, d2=0.666 g=0.789\n",
      ">4, 13/468, d1=0.673, d2=0.654 g=0.821\n",
      ">4, 14/468, d1=0.703, d2=0.615 g=0.817\n",
      ">4, 15/468, d1=0.729, d2=0.630 g=0.824\n",
      ">4, 16/468, d1=0.707, d2=0.617 g=0.806\n",
      ">4, 17/468, d1=0.657, d2=0.633 g=0.822\n",
      ">4, 18/468, d1=0.675, d2=0.656 g=0.800\n",
      ">4, 19/468, d1=0.722, d2=0.647 g=0.762\n",
      ">4, 20/468, d1=0.653, d2=0.673 g=0.752\n",
      ">4, 21/468, d1=0.692, d2=0.681 g=0.745\n",
      ">4, 22/468, d1=0.713, d2=0.703 g=0.739\n",
      ">4, 23/468, d1=0.676, d2=0.670 g=0.722\n",
      ">4, 24/468, d1=0.663, d2=0.682 g=0.743\n",
      ">4, 25/468, d1=0.675, d2=0.667 g=0.752\n",
      ">4, 26/468, d1=0.679, d2=0.652 g=0.781\n",
      ">4, 27/468, d1=0.654, d2=0.656 g=0.785\n",
      ">4, 28/468, d1=0.666, d2=0.635 g=0.796\n",
      ">4, 29/468, d1=0.674, d2=0.654 g=0.801\n",
      ">4, 30/468, d1=0.628, d2=0.629 g=0.803\n",
      ">4, 31/468, d1=0.699, d2=0.648 g=0.786\n",
      ">4, 32/468, d1=0.659, d2=0.653 g=0.788\n",
      ">4, 33/468, d1=0.650, d2=0.671 g=0.741\n",
      ">4, 34/468, d1=0.647, d2=0.683 g=0.726\n",
      ">4, 35/468, d1=0.659, d2=0.713 g=0.711\n",
      ">4, 36/468, d1=0.633, d2=0.726 g=0.709\n",
      ">4, 37/468, d1=0.670, d2=0.703 g=0.725\n",
      ">4, 38/468, d1=0.642, d2=0.709 g=0.747\n",
      ">4, 39/468, d1=0.678, d2=0.683 g=0.776\n",
      ">4, 40/468, d1=0.695, d2=0.652 g=0.798\n",
      ">4, 41/468, d1=0.675, d2=0.620 g=0.836\n",
      ">4, 42/468, d1=0.662, d2=0.614 g=0.862\n",
      ">4, 43/468, d1=0.664, d2=0.595 g=0.850\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">4, 44/468, d1=0.691, d2=0.616 g=0.849\n",
      ">4, 45/468, d1=0.664, d2=0.672 g=0.821\n",
      ">4, 46/468, d1=0.701, d2=0.691 g=0.759\n",
      ">4, 47/468, d1=0.680, d2=0.681 g=0.735\n",
      ">4, 48/468, d1=0.667, d2=0.704 g=0.724\n",
      ">4, 49/468, d1=0.673, d2=0.707 g=0.704\n",
      ">4, 50/468, d1=0.667, d2=0.715 g=0.725\n",
      ">4, 51/468, d1=0.646, d2=0.695 g=0.737\n",
      ">4, 52/468, d1=0.656, d2=0.656 g=0.755\n",
      ">4, 53/468, d1=0.668, d2=0.643 g=0.790\n",
      ">4, 54/468, d1=0.674, d2=0.648 g=0.787\n",
      ">4, 55/468, d1=0.646, d2=0.645 g=0.802\n",
      ">4, 56/468, d1=0.643, d2=0.638 g=0.800\n",
      ">4, 57/468, d1=0.662, d2=0.635 g=0.789\n",
      ">4, 58/468, d1=0.651, d2=0.652 g=0.793\n",
      ">4, 59/468, d1=0.656, d2=0.660 g=0.774\n",
      ">4, 60/468, d1=0.670, d2=0.678 g=0.742\n",
      ">4, 61/468, d1=0.631, d2=0.702 g=0.724\n",
      ">4, 62/468, d1=0.642, d2=0.696 g=0.730\n",
      ">4, 63/468, d1=0.644, d2=0.711 g=0.750\n",
      ">4, 64/468, d1=0.654, d2=0.681 g=0.743\n",
      ">4, 65/468, d1=0.685, d2=0.689 g=0.779\n",
      ">4, 66/468, d1=0.655, d2=0.651 g=0.783\n",
      ">4, 67/468, d1=0.658, d2=0.650 g=0.813\n",
      ">4, 68/468, d1=0.675, d2=0.634 g=0.800\n",
      ">4, 69/468, d1=0.678, d2=0.622 g=0.811\n",
      ">4, 70/468, d1=0.676, d2=0.619 g=0.821\n",
      ">4, 71/468, d1=0.682, d2=0.649 g=0.799\n",
      ">4, 72/468, d1=0.704, d2=0.639 g=0.771\n",
      ">4, 73/468, d1=0.671, d2=0.648 g=0.765\n",
      ">4, 74/468, d1=0.657, d2=0.638 g=0.738\n",
      ">4, 75/468, d1=0.678, d2=0.692 g=0.736\n",
      ">4, 76/468, d1=0.660, d2=0.693 g=0.739\n",
      ">4, 77/468, d1=0.646, d2=0.696 g=0.741\n",
      ">4, 78/468, d1=0.651, d2=0.716 g=0.741\n",
      ">4, 79/468, d1=0.659, d2=0.708 g=0.746\n",
      ">4, 80/468, d1=0.634, d2=0.657 g=0.753\n",
      ">4, 81/468, d1=0.652, d2=0.666 g=0.769\n",
      ">4, 82/468, d1=0.687, d2=0.639 g=0.782\n",
      ">4, 83/468, d1=0.681, d2=0.622 g=0.787\n",
      ">4, 84/468, d1=0.654, d2=0.629 g=0.784\n",
      ">4, 85/468, d1=0.657, d2=0.637 g=0.792\n",
      ">4, 86/468, d1=0.624, d2=0.646 g=0.802\n",
      ">4, 87/468, d1=0.648, d2=0.656 g=0.764\n",
      ">4, 88/468, d1=0.610, d2=0.668 g=0.754\n",
      ">4, 89/468, d1=0.637, d2=0.661 g=0.727\n",
      ">4, 90/468, d1=0.630, d2=0.696 g=0.742\n",
      ">4, 91/468, d1=0.627, d2=0.723 g=0.730\n",
      ">4, 92/468, d1=0.672, d2=0.707 g=0.744\n",
      ">4, 93/468, d1=0.632, d2=0.694 g=0.739\n",
      ">4, 94/468, d1=0.669, d2=0.660 g=0.759\n",
      ">4, 95/468, d1=0.692, d2=0.669 g=0.781\n",
      ">4, 96/468, d1=0.678, d2=0.633 g=0.797\n",
      ">4, 97/468, d1=0.675, d2=0.636 g=0.809\n",
      ">4, 98/468, d1=0.687, d2=0.605 g=0.839\n",
      ">4, 99/468, d1=0.638, d2=0.609 g=0.848\n",
      ">4, 100/468, d1=0.710, d2=0.614 g=0.841\n",
      ">4, 101/468, d1=0.680, d2=0.629 g=0.797\n",
      ">4, 102/468, d1=0.706, d2=0.679 g=0.773\n",
      ">4, 103/468, d1=0.655, d2=0.707 g=0.763\n",
      ">4, 104/468, d1=0.684, d2=0.693 g=0.742\n",
      ">4, 105/468, d1=0.699, d2=0.699 g=0.734\n",
      ">4, 106/468, d1=0.681, d2=0.683 g=0.732\n",
      ">4, 107/468, d1=0.691, d2=0.670 g=0.767\n",
      ">4, 108/468, d1=0.659, d2=0.648 g=0.787\n",
      ">4, 109/468, d1=0.661, d2=0.639 g=0.829\n",
      ">4, 110/468, d1=0.660, d2=0.592 g=0.830\n",
      ">4, 111/468, d1=0.662, d2=0.589 g=0.873\n",
      ">4, 112/468, d1=0.691, d2=0.596 g=0.893\n",
      ">4, 113/468, d1=0.663, d2=0.591 g=0.858\n",
      ">4, 114/468, d1=0.656, d2=0.607 g=0.860\n",
      ">4, 115/468, d1=0.628, d2=0.629 g=0.809\n",
      ">4, 116/468, d1=0.648, d2=0.665 g=0.780\n",
      ">4, 117/468, d1=0.661, d2=0.739 g=0.750\n",
      ">4, 118/468, d1=0.611, d2=0.768 g=0.731\n",
      ">4, 119/468, d1=0.706, d2=0.741 g=0.744\n",
      ">4, 120/468, d1=0.679, d2=0.664 g=0.794\n",
      ">4, 121/468, d1=0.671, d2=0.635 g=0.786\n",
      ">4, 122/468, d1=0.687, d2=0.615 g=0.836\n",
      ">4, 123/468, d1=0.666, d2=0.604 g=0.886\n",
      ">4, 124/468, d1=0.705, d2=0.585 g=0.917\n",
      ">4, 125/468, d1=0.706, d2=0.556 g=0.902\n",
      ">4, 126/468, d1=0.763, d2=0.603 g=0.826\n",
      ">4, 127/468, d1=0.669, d2=0.654 g=0.822\n",
      ">4, 128/468, d1=0.667, d2=0.614 g=0.780\n",
      ">4, 129/468, d1=0.668, d2=0.662 g=0.760\n",
      ">4, 130/468, d1=0.647, d2=0.705 g=0.756\n",
      ">4, 131/468, d1=0.715, d2=0.728 g=0.747\n",
      ">4, 132/468, d1=0.637, d2=0.708 g=0.731\n",
      ">4, 133/468, d1=0.671, d2=0.672 g=0.744\n",
      ">4, 134/468, d1=0.676, d2=0.679 g=0.770\n",
      ">4, 135/468, d1=0.656, d2=0.667 g=0.800\n",
      ">4, 136/468, d1=0.631, d2=0.650 g=0.823\n",
      ">4, 137/468, d1=0.651, d2=0.604 g=0.856\n",
      ">4, 138/468, d1=0.662, d2=0.580 g=0.879\n",
      ">4, 139/468, d1=0.643, d2=0.588 g=0.881\n",
      ">4, 140/468, d1=0.670, d2=0.601 g=0.868\n",
      ">4, 141/468, d1=0.633, d2=0.629 g=0.830\n",
      ">4, 142/468, d1=0.682, d2=0.658 g=0.775\n",
      ">4, 143/468, d1=0.642, d2=0.658 g=0.782\n",
      ">4, 144/468, d1=0.627, d2=0.699 g=0.750\n",
      ">4, 145/468, d1=0.646, d2=0.701 g=0.753\n",
      ">4, 146/468, d1=0.660, d2=0.696 g=0.749\n",
      ">4, 147/468, d1=0.665, d2=0.731 g=0.740\n",
      ">4, 148/468, d1=0.657, d2=0.691 g=0.803\n",
      ">4, 149/468, d1=0.674, d2=0.672 g=0.801\n",
      ">4, 150/468, d1=0.659, d2=0.631 g=0.826\n",
      ">4, 151/468, d1=0.705, d2=0.626 g=0.860\n",
      ">4, 152/468, d1=0.665, d2=0.603 g=0.861\n",
      ">4, 153/468, d1=0.688, d2=0.613 g=0.864\n",
      ">4, 154/468, d1=0.684, d2=0.587 g=0.883\n",
      ">4, 155/468, d1=0.713, d2=0.594 g=0.836\n",
      ">4, 156/468, d1=0.697, d2=0.655 g=0.798\n",
      ">4, 157/468, d1=0.716, d2=0.687 g=0.760\n",
      ">4, 158/468, d1=0.711, d2=0.687 g=0.753\n",
      ">4, 159/468, d1=0.665, d2=0.678 g=0.744\n",
      ">4, 160/468, d1=0.671, d2=0.703 g=0.738\n",
      ">4, 161/468, d1=0.708, d2=0.690 g=0.762\n",
      ">4, 162/468, d1=0.680, d2=0.652 g=0.786\n",
      ">4, 163/468, d1=0.654, d2=0.628 g=0.814\n",
      ">4, 164/468, d1=0.663, d2=0.621 g=0.825\n",
      ">4, 165/468, d1=0.625, d2=0.587 g=0.855\n",
      ">4, 166/468, d1=0.615, d2=0.600 g=0.883\n",
      ">4, 167/468, d1=0.603, d2=0.591 g=0.889\n",
      ">4, 168/468, d1=0.678, d2=0.602 g=0.855\n",
      ">4, 169/468, d1=0.695, d2=0.639 g=0.834\n",
      ">4, 170/468, d1=0.660, d2=0.663 g=0.783\n",
      ">4, 171/468, d1=0.654, d2=0.715 g=0.726\n",
      ">4, 172/468, d1=0.632, d2=0.734 g=0.737\n",
      ">4, 173/468, d1=0.648, d2=0.706 g=0.740\n",
      ">4, 174/468, d1=0.686, d2=0.665 g=0.744\n",
      ">4, 175/468, d1=0.670, d2=0.682 g=0.765\n",
      ">4, 176/468, d1=0.655, d2=0.657 g=0.795\n",
      ">4, 177/468, d1=0.713, d2=0.650 g=0.817\n",
      ">4, 178/468, d1=0.684, d2=0.625 g=0.823\n",
      ">4, 179/468, d1=0.708, d2=0.604 g=0.852\n",
      ">4, 180/468, d1=0.701, d2=0.629 g=0.833\n",
      ">4, 181/468, d1=0.688, d2=0.604 g=0.854\n",
      ">4, 182/468, d1=0.631, d2=0.619 g=0.788\n",
      ">4, 183/468, d1=0.671, d2=0.651 g=0.801\n",
      ">4, 184/468, d1=0.696, d2=0.668 g=0.741\n",
      ">4, 185/468, d1=0.666, d2=0.692 g=0.727\n",
      ">4, 186/468, d1=0.677, d2=0.689 g=0.730\n",
      ">4, 187/468, d1=0.642, d2=0.686 g=0.711\n",
      ">4, 188/468, d1=0.666, d2=0.681 g=0.724\n",
      ">4, 189/468, d1=0.672, d2=0.707 g=0.722\n",
      ">4, 190/468, d1=0.666, d2=0.670 g=0.760\n",
      ">4, 191/468, d1=0.669, d2=0.671 g=0.775\n",
      ">4, 192/468, d1=0.667, d2=0.646 g=0.781\n",
      ">4, 193/468, d1=0.656, d2=0.619 g=0.796\n",
      ">4, 194/468, d1=0.626, d2=0.635 g=0.807\n",
      ">4, 195/468, d1=0.616, d2=0.615 g=0.835\n",
      ">4, 196/468, d1=0.641, d2=0.622 g=0.827\n",
      ">4, 197/468, d1=0.630, d2=0.639 g=0.813\n",
      ">4, 198/468, d1=0.601, d2=0.632 g=0.772\n",
      ">4, 199/468, d1=0.657, d2=0.664 g=0.764\n",
      ">4, 200/468, d1=0.623, d2=0.705 g=0.722\n",
      ">4, 201/468, d1=0.671, d2=0.726 g=0.701\n",
      ">4, 202/468, d1=0.651, d2=0.731 g=0.693\n",
      ">4, 203/468, d1=0.618, d2=0.764 g=0.733\n",
      ">4, 204/468, d1=0.659, d2=0.685 g=0.729\n",
      ">4, 205/468, d1=0.674, d2=0.683 g=0.758\n",
      ">4, 206/468, d1=0.653, d2=0.646 g=0.804\n",
      ">4, 207/468, d1=0.674, d2=0.619 g=0.844\n",
      ">4, 208/468, d1=0.692, d2=0.589 g=0.875\n",
      ">4, 209/468, d1=0.702, d2=0.577 g=0.864\n",
      ">4, 210/468, d1=0.705, d2=0.601 g=0.845\n",
      ">4, 211/468, d1=0.700, d2=0.608 g=0.834\n",
      ">4, 212/468, d1=0.696, d2=0.626 g=0.805\n",
      ">4, 213/468, d1=0.640, d2=0.677 g=0.754\n",
      ">4, 214/468, d1=0.682, d2=0.724 g=0.764\n",
      ">4, 215/468, d1=0.627, d2=0.720 g=0.741\n",
      ">4, 216/468, d1=0.676, d2=0.692 g=0.741\n",
      ">4, 217/468, d1=0.661, d2=0.680 g=0.742\n",
      ">4, 218/468, d1=0.645, d2=0.685 g=0.743\n",
      ">4, 219/468, d1=0.661, d2=0.682 g=0.751\n",
      ">4, 220/468, d1=0.629, d2=0.656 g=0.762\n",
      ">4, 221/468, d1=0.664, d2=0.654 g=0.786\n",
      ">4, 222/468, d1=0.666, d2=0.645 g=0.777\n",
      ">4, 223/468, d1=0.656, d2=0.661 g=0.795\n",
      ">4, 224/468, d1=0.635, d2=0.644 g=0.766\n",
      ">4, 225/468, d1=0.679, d2=0.676 g=0.758\n",
      ">4, 226/468, d1=0.649, d2=0.669 g=0.767\n",
      ">4, 227/468, d1=0.610, d2=0.691 g=0.741\n",
      ">4, 228/468, d1=0.673, d2=0.693 g=0.740\n",
      ">4, 229/468, d1=0.633, d2=0.685 g=0.760\n",
      ">4, 230/468, d1=0.660, d2=0.690 g=0.765\n",
      ">4, 231/468, d1=0.660, d2=0.670 g=0.763\n",
      ">4, 232/468, d1=0.668, d2=0.670 g=0.778\n",
      ">4, 233/468, d1=0.669, d2=0.664 g=0.783\n",
      ">4, 234/468, d1=0.686, d2=0.649 g=0.781\n",
      ">4, 235/468, d1=0.659, d2=0.623 g=0.811\n",
      ">4, 236/468, d1=0.653, d2=0.606 g=0.801\n",
      ">4, 237/468, d1=0.668, d2=0.649 g=0.808\n",
      ">4, 238/468, d1=0.686, d2=0.614 g=0.832\n",
      ">4, 239/468, d1=0.698, d2=0.616 g=0.806\n",
      ">4, 240/468, d1=0.688, d2=0.698 g=0.769\n",
      ">4, 241/468, d1=0.690, d2=0.653 g=0.765\n",
      ">4, 242/468, d1=0.686, d2=0.663 g=0.747\n",
      ">4, 243/468, d1=0.664, d2=0.663 g=0.750\n",
      ">4, 244/468, d1=0.669, d2=0.688 g=0.739\n",
      ">4, 245/468, d1=0.648, d2=0.677 g=0.740\n",
      ">4, 246/468, d1=0.647, d2=0.683 g=0.746\n",
      ">4, 247/468, d1=0.684, d2=0.668 g=0.768\n",
      ">4, 248/468, d1=0.650, d2=0.677 g=0.768\n",
      ">4, 249/468, d1=0.641, d2=0.652 g=0.753\n",
      ">4, 250/468, d1=0.642, d2=0.661 g=0.780\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">4, 251/468, d1=0.640, d2=0.678 g=0.771\n",
      ">4, 252/468, d1=0.644, d2=0.668 g=0.763\n",
      ">4, 253/468, d1=0.655, d2=0.667 g=0.772\n",
      ">4, 254/468, d1=0.643, d2=0.669 g=0.759\n",
      ">4, 255/468, d1=0.649, d2=0.671 g=0.747\n",
      ">4, 256/468, d1=0.649, d2=0.666 g=0.754\n",
      ">4, 257/468, d1=0.659, d2=0.686 g=0.773\n",
      ">4, 258/468, d1=0.679, d2=0.667 g=0.771\n",
      ">4, 259/468, d1=0.689, d2=0.659 g=0.756\n",
      ">4, 260/468, d1=0.660, d2=0.642 g=0.778\n",
      ">4, 261/468, d1=0.690, d2=0.643 g=0.789\n",
      ">4, 262/468, d1=0.676, d2=0.670 g=0.793\n",
      ">4, 263/468, d1=0.681, d2=0.665 g=0.794\n",
      ">4, 264/468, d1=0.688, d2=0.646 g=0.798\n",
      ">4, 265/468, d1=0.698, d2=0.637 g=0.797\n",
      ">4, 266/468, d1=0.699, d2=0.612 g=0.802\n",
      ">4, 267/468, d1=0.655, d2=0.645 g=0.796\n",
      ">4, 268/468, d1=0.663, d2=0.635 g=0.767\n",
      ">4, 269/468, d1=0.694, d2=0.656 g=0.757\n",
      ">4, 270/468, d1=0.683, d2=0.657 g=0.756\n",
      ">4, 271/468, d1=0.704, d2=0.664 g=0.760\n",
      ">4, 272/468, d1=0.655, d2=0.671 g=0.759\n",
      ">4, 273/468, d1=0.659, d2=0.678 g=0.776\n",
      ">4, 274/468, d1=0.662, d2=0.654 g=0.767\n",
      ">4, 275/468, d1=0.643, d2=0.646 g=0.783\n",
      ">4, 276/468, d1=0.635, d2=0.656 g=0.764\n",
      ">4, 277/468, d1=0.636, d2=0.651 g=0.798\n",
      ">4, 278/468, d1=0.675, d2=0.657 g=0.778\n",
      ">4, 279/468, d1=0.644, d2=0.662 g=0.771\n",
      ">4, 280/468, d1=0.673, d2=0.665 g=0.783\n",
      ">4, 281/468, d1=0.631, d2=0.677 g=0.761\n",
      ">4, 282/468, d1=0.650, d2=0.668 g=0.775\n",
      ">4, 283/468, d1=0.652, d2=0.649 g=0.753\n",
      ">4, 284/468, d1=0.677, d2=0.662 g=0.786\n",
      ">4, 285/468, d1=0.676, d2=0.680 g=0.781\n",
      ">4, 286/468, d1=0.673, d2=0.662 g=0.755\n",
      ">4, 287/468, d1=0.675, d2=0.654 g=0.780\n",
      ">4, 288/468, d1=0.650, d2=0.647 g=0.789\n",
      ">4, 289/468, d1=0.661, d2=0.646 g=0.792\n",
      ">4, 290/468, d1=0.674, d2=0.595 g=0.810\n",
      ">4, 291/468, d1=0.687, d2=0.610 g=0.816\n",
      ">4, 292/468, d1=0.682, d2=0.637 g=0.794\n",
      ">4, 293/468, d1=0.691, d2=0.632 g=0.779\n",
      ">4, 294/468, d1=0.668, d2=0.635 g=0.788\n",
      ">4, 295/468, d1=0.673, d2=0.651 g=0.773\n",
      ">4, 296/468, d1=0.674, d2=0.675 g=0.766\n",
      ">4, 297/468, d1=0.702, d2=0.672 g=0.767\n",
      ">4, 298/468, d1=0.666, d2=0.652 g=0.783\n",
      ">4, 299/468, d1=0.666, d2=0.644 g=0.788\n",
      ">4, 300/468, d1=0.662, d2=0.649 g=0.791\n",
      ">4, 301/468, d1=0.671, d2=0.661 g=0.784\n",
      ">4, 302/468, d1=0.667, d2=0.649 g=0.774\n",
      ">4, 303/468, d1=0.684, d2=0.651 g=0.766\n",
      ">4, 304/468, d1=0.639, d2=0.629 g=0.773\n",
      ">4, 305/468, d1=0.651, d2=0.661 g=0.772\n",
      ">4, 306/468, d1=0.670, d2=0.670 g=0.769\n",
      ">4, 307/468, d1=0.667, d2=0.665 g=0.750\n",
      ">4, 308/468, d1=0.647, d2=0.672 g=0.755\n",
      ">4, 309/468, d1=0.674, d2=0.684 g=0.776\n",
      ">4, 310/468, d1=0.660, d2=0.659 g=0.751\n",
      ">4, 311/468, d1=0.681, d2=0.676 g=0.764\n",
      ">4, 312/468, d1=0.673, d2=0.712 g=0.790\n",
      ">4, 313/468, d1=0.679, d2=0.640 g=0.806\n",
      ">4, 314/468, d1=0.679, d2=0.653 g=0.795\n",
      ">4, 315/468, d1=0.687, d2=0.636 g=0.838\n",
      ">4, 316/468, d1=0.707, d2=0.610 g=0.830\n",
      ">4, 317/468, d1=0.696, d2=0.610 g=0.821\n",
      ">4, 318/468, d1=0.667, d2=0.598 g=0.835\n",
      ">4, 319/468, d1=0.692, d2=0.698 g=0.785\n",
      ">4, 320/468, d1=0.722, d2=0.678 g=0.756\n",
      ">4, 321/468, d1=0.683, d2=0.675 g=0.735\n",
      ">4, 322/468, d1=0.680, d2=0.687 g=0.739\n",
      ">4, 323/468, d1=0.666, d2=0.709 g=0.751\n",
      ">4, 324/468, d1=0.646, d2=0.657 g=0.783\n",
      ">4, 325/468, d1=0.669, d2=0.647 g=0.765\n",
      ">4, 326/468, d1=0.685, d2=0.643 g=0.788\n",
      ">4, 327/468, d1=0.663, d2=0.653 g=0.786\n",
      ">4, 328/468, d1=0.648, d2=0.682 g=0.766\n",
      ">4, 329/468, d1=0.667, d2=0.670 g=0.765\n",
      ">4, 330/468, d1=0.668, d2=0.691 g=0.745\n",
      ">4, 331/468, d1=0.645, d2=0.697 g=0.722\n",
      ">4, 332/468, d1=0.650, d2=0.699 g=0.747\n",
      ">4, 333/468, d1=0.679, d2=0.691 g=0.767\n",
      ">4, 334/468, d1=0.693, d2=0.645 g=0.764\n",
      ">4, 335/468, d1=0.677, d2=0.637 g=0.793\n",
      ">4, 336/468, d1=0.690, d2=0.655 g=0.807\n",
      ">4, 337/468, d1=0.684, d2=0.644 g=0.793\n",
      ">4, 338/468, d1=0.686, d2=0.626 g=0.822\n",
      ">4, 339/468, d1=0.677, d2=0.632 g=0.805\n",
      ">4, 340/468, d1=0.708, d2=0.671 g=0.783\n",
      ">4, 341/468, d1=0.669, d2=0.654 g=0.789\n",
      ">4, 342/468, d1=0.661, d2=0.659 g=0.777\n",
      ">4, 343/468, d1=0.663, d2=0.658 g=0.770\n",
      ">4, 344/468, d1=0.646, d2=0.651 g=0.753\n",
      ">4, 345/468, d1=0.655, d2=0.634 g=0.766\n",
      ">4, 346/468, d1=0.652, d2=0.656 g=0.769\n",
      ">4, 347/468, d1=0.667, d2=0.648 g=0.775\n",
      ">4, 348/468, d1=0.675, d2=0.666 g=0.772\n",
      ">4, 349/468, d1=0.660, d2=0.655 g=0.768\n",
      ">4, 350/468, d1=0.654, d2=0.675 g=0.769\n",
      ">4, 351/468, d1=0.660, d2=0.677 g=0.754\n",
      ">4, 352/468, d1=0.630, d2=0.685 g=0.753\n",
      ">4, 353/468, d1=0.684, d2=0.689 g=0.750\n",
      ">4, 354/468, d1=0.673, d2=0.706 g=0.769\n",
      ">4, 355/468, d1=0.687, d2=0.683 g=0.797\n",
      ">4, 356/468, d1=0.687, d2=0.648 g=0.790\n",
      ">4, 357/468, d1=0.666, d2=0.625 g=0.806\n",
      ">4, 358/468, d1=0.687, d2=0.613 g=0.821\n",
      ">4, 359/468, d1=0.677, d2=0.605 g=0.829\n",
      ">4, 360/468, d1=0.699, d2=0.647 g=0.841\n",
      ">4, 361/468, d1=0.685, d2=0.644 g=0.811\n",
      ">4, 362/468, d1=0.717, d2=0.662 g=0.799\n",
      ">4, 363/468, d1=0.702, d2=0.667 g=0.784\n",
      ">4, 364/468, d1=0.673, d2=0.674 g=0.766\n",
      ">4, 365/468, d1=0.663, d2=0.694 g=0.755\n",
      ">4, 366/468, d1=0.694, d2=0.673 g=0.760\n",
      ">4, 367/468, d1=0.654, d2=0.665 g=0.784\n",
      ">4, 368/468, d1=0.697, d2=0.645 g=0.783\n",
      ">4, 369/468, d1=0.654, d2=0.652 g=0.787\n",
      ">4, 370/468, d1=0.677, d2=0.645 g=0.781\n",
      ">4, 371/468, d1=0.626, d2=0.678 g=0.767\n",
      ">4, 372/468, d1=0.634, d2=0.665 g=0.766\n",
      ">4, 373/468, d1=0.647, d2=0.674 g=0.744\n",
      ">4, 374/468, d1=0.673, d2=0.694 g=0.762\n",
      ">4, 375/468, d1=0.706, d2=0.697 g=0.741\n",
      ">4, 376/468, d1=0.660, d2=0.692 g=0.769\n",
      ">4, 377/468, d1=0.673, d2=0.654 g=0.793\n",
      ">4, 378/468, d1=0.667, d2=0.631 g=0.792\n",
      ">4, 379/468, d1=0.683, d2=0.653 g=0.782\n",
      ">4, 380/468, d1=0.667, d2=0.640 g=0.813\n",
      ">4, 381/468, d1=0.711, d2=0.614 g=0.808\n",
      ">4, 382/468, d1=0.694, d2=0.666 g=0.789\n",
      ">4, 383/468, d1=0.699, d2=0.666 g=0.782\n",
      ">4, 384/468, d1=0.703, d2=0.675 g=0.742\n",
      ">4, 385/468, d1=0.669, d2=0.665 g=0.756\n",
      ">4, 386/468, d1=0.688, d2=0.690 g=0.743\n",
      ">4, 387/468, d1=0.666, d2=0.683 g=0.757\n",
      ">4, 388/468, d1=0.671, d2=0.687 g=0.769\n",
      ">4, 389/468, d1=0.672, d2=0.644 g=0.790\n",
      ">4, 390/468, d1=0.652, d2=0.658 g=0.779\n",
      ">4, 391/468, d1=0.673, d2=0.627 g=0.777\n",
      ">4, 392/468, d1=0.670, d2=0.665 g=0.749\n",
      ">4, 393/468, d1=0.647, d2=0.710 g=0.722\n",
      ">4, 394/468, d1=0.633, d2=0.708 g=0.699\n",
      ">4, 395/468, d1=0.646, d2=0.728 g=0.724\n",
      ">4, 396/468, d1=0.648, d2=0.717 g=0.751\n",
      ">4, 397/468, d1=0.660, d2=0.702 g=0.784\n",
      ">4, 398/468, d1=0.669, d2=0.635 g=0.786\n",
      ">4, 399/468, d1=0.690, d2=0.637 g=0.784\n",
      ">4, 400/468, d1=0.686, d2=0.642 g=0.818\n",
      ">4, 401/468, d1=0.651, d2=0.656 g=0.816\n",
      ">4, 402/468, d1=0.691, d2=0.657 g=0.803\n",
      ">4, 403/468, d1=0.684, d2=0.643 g=0.790\n",
      ">4, 404/468, d1=0.706, d2=0.660 g=0.767\n",
      ">4, 405/468, d1=0.700, d2=0.677 g=0.751\n",
      ">4, 406/468, d1=0.697, d2=0.698 g=0.742\n",
      ">4, 407/468, d1=0.683, d2=0.681 g=0.735\n",
      ">4, 408/468, d1=0.679, d2=0.668 g=0.747\n",
      ">4, 409/468, d1=0.656, d2=0.655 g=0.760\n",
      ">4, 410/468, d1=0.632, d2=0.657 g=0.761\n",
      ">4, 411/468, d1=0.675, d2=0.684 g=0.775\n",
      ">4, 412/468, d1=0.644, d2=0.662 g=0.756\n",
      ">4, 413/468, d1=0.661, d2=0.661 g=0.733\n",
      ">4, 414/468, d1=0.695, d2=0.686 g=0.733\n",
      ">4, 415/468, d1=0.611, d2=0.689 g=0.729\n",
      ">4, 416/468, d1=0.635, d2=0.680 g=0.755\n",
      ">4, 417/468, d1=0.700, d2=0.691 g=0.758\n",
      ">4, 418/468, d1=0.658, d2=0.686 g=0.768\n",
      ">4, 419/468, d1=0.673, d2=0.686 g=0.780\n",
      ">4, 420/468, d1=0.655, d2=0.636 g=0.801\n",
      ">4, 421/468, d1=0.672, d2=0.649 g=0.801\n",
      ">4, 422/468, d1=0.670, d2=0.645 g=0.809\n",
      ">4, 423/468, d1=0.670, d2=0.626 g=0.772\n",
      ">4, 424/468, d1=0.689, d2=0.662 g=0.763\n",
      ">4, 425/468, d1=0.669, d2=0.676 g=0.782\n",
      ">4, 426/468, d1=0.677, d2=0.674 g=0.756\n",
      ">4, 427/468, d1=0.691, d2=0.669 g=0.731\n",
      ">4, 428/468, d1=0.688, d2=0.694 g=0.732\n",
      ">4, 429/468, d1=0.631, d2=0.681 g=0.735\n",
      ">4, 430/468, d1=0.645, d2=0.686 g=0.744\n",
      ">4, 431/468, d1=0.646, d2=0.672 g=0.753\n",
      ">4, 432/468, d1=0.675, d2=0.669 g=0.777\n",
      ">4, 433/468, d1=0.623, d2=0.681 g=0.742\n",
      ">4, 434/468, d1=0.651, d2=0.691 g=0.754\n",
      ">4, 435/468, d1=0.659, d2=0.682 g=0.731\n",
      ">4, 436/468, d1=0.626, d2=0.697 g=0.745\n",
      ">4, 437/468, d1=0.639, d2=0.709 g=0.740\n",
      ">4, 438/468, d1=0.703, d2=0.697 g=0.743\n",
      ">4, 439/468, d1=0.697, d2=0.700 g=0.755\n",
      ">4, 440/468, d1=0.673, d2=0.673 g=0.784\n",
      ">4, 441/468, d1=0.681, d2=0.620 g=0.785\n",
      ">4, 442/468, d1=0.679, d2=0.625 g=0.814\n",
      ">4, 443/468, d1=0.695, d2=0.618 g=0.808\n",
      ">4, 444/468, d1=0.705, d2=0.652 g=0.820\n",
      ">4, 445/468, d1=0.693, d2=0.672 g=0.781\n",
      ">4, 446/468, d1=0.687, d2=0.680 g=0.763\n",
      ">4, 447/468, d1=0.681, d2=0.655 g=0.766\n",
      ">4, 448/468, d1=0.712, d2=0.683 g=0.752\n",
      ">4, 449/468, d1=0.681, d2=0.681 g=0.729\n",
      ">4, 450/468, d1=0.686, d2=0.672 g=0.752\n",
      ">4, 451/468, d1=0.673, d2=0.686 g=0.738\n",
      ">4, 452/468, d1=0.653, d2=0.661 g=0.764\n",
      ">4, 453/468, d1=0.658, d2=0.647 g=0.783\n",
      ">4, 454/468, d1=0.668, d2=0.647 g=0.769\n",
      ">4, 455/468, d1=0.617, d2=0.666 g=0.761\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">4, 456/468, d1=0.627, d2=0.693 g=0.743\n",
      ">4, 457/468, d1=0.639, d2=0.687 g=0.712\n",
      ">4, 458/468, d1=0.662, d2=0.743 g=0.718\n",
      ">4, 459/468, d1=0.685, d2=0.727 g=0.726\n",
      ">4, 460/468, d1=0.682, d2=0.693 g=0.748\n",
      ">4, 461/468, d1=0.682, d2=0.681 g=0.773\n",
      ">4, 462/468, d1=0.669, d2=0.651 g=0.776\n",
      ">4, 463/468, d1=0.675, d2=0.643 g=0.796\n",
      ">4, 464/468, d1=0.706, d2=0.658 g=0.821\n",
      ">4, 465/468, d1=0.731, d2=0.654 g=0.769\n",
      ">4, 466/468, d1=0.669, d2=0.639 g=0.763\n",
      ">4, 467/468, d1=0.713, d2=0.693 g=0.758\n",
      ">4, 468/468, d1=0.678, d2=0.673 g=0.753\n",
      ">5, 1/468, d1=0.678, d2=0.673 g=0.742\n",
      ">5, 2/468, d1=0.682, d2=0.698 g=0.744\n",
      ">5, 3/468, d1=0.651, d2=0.672 g=0.756\n",
      ">5, 4/468, d1=0.640, d2=0.677 g=0.755\n",
      ">5, 5/468, d1=0.650, d2=0.657 g=0.789\n",
      ">5, 6/468, d1=0.645, d2=0.677 g=0.757\n",
      ">5, 7/468, d1=0.672, d2=0.694 g=0.756\n",
      ">5, 8/468, d1=0.664, d2=0.696 g=0.735\n",
      ">5, 9/468, d1=0.670, d2=0.676 g=0.737\n",
      ">5, 10/468, d1=0.640, d2=0.714 g=0.715\n",
      ">5, 11/468, d1=0.640, d2=0.713 g=0.720\n",
      ">5, 12/468, d1=0.682, d2=0.703 g=0.735\n",
      ">5, 13/468, d1=0.677, d2=0.661 g=0.772\n",
      ">5, 14/468, d1=0.678, d2=0.649 g=0.792\n",
      ">5, 15/468, d1=0.690, d2=0.671 g=0.816\n",
      ">5, 16/468, d1=0.687, d2=0.626 g=0.807\n",
      ">5, 17/468, d1=0.660, d2=0.636 g=0.827\n",
      ">5, 18/468, d1=0.709, d2=0.633 g=0.796\n",
      ">5, 19/468, d1=0.666, d2=0.653 g=0.775\n",
      ">5, 20/468, d1=0.675, d2=0.644 g=0.784\n",
      ">5, 21/468, d1=0.707, d2=0.661 g=0.765\n",
      ">5, 22/468, d1=0.674, d2=0.690 g=0.742\n",
      ">5, 23/468, d1=0.685, d2=0.699 g=0.753\n",
      ">5, 24/468, d1=0.671, d2=0.686 g=0.745\n",
      ">5, 25/468, d1=0.676, d2=0.662 g=0.749\n",
      ">5, 26/468, d1=0.660, d2=0.670 g=0.774\n",
      ">5, 27/468, d1=0.639, d2=0.639 g=0.776\n",
      ">5, 28/468, d1=0.668, d2=0.661 g=0.779\n",
      ">5, 29/468, d1=0.692, d2=0.651 g=0.761\n",
      ">5, 30/468, d1=0.672, d2=0.672 g=0.723\n",
      ">5, 31/468, d1=0.658, d2=0.682 g=0.732\n",
      ">5, 32/468, d1=0.649, d2=0.690 g=0.728\n",
      ">5, 33/468, d1=0.660, d2=0.715 g=0.736\n",
      ">5, 34/468, d1=0.663, d2=0.680 g=0.751\n",
      ">5, 35/468, d1=0.684, d2=0.694 g=0.762\n",
      ">5, 36/468, d1=0.652, d2=0.652 g=0.791\n",
      ">5, 37/468, d1=0.681, d2=0.639 g=0.797\n",
      ">5, 38/468, d1=0.664, d2=0.622 g=0.816\n",
      ">5, 39/468, d1=0.693, d2=0.626 g=0.805\n",
      ">5, 40/468, d1=0.678, d2=0.653 g=0.786\n",
      ">5, 41/468, d1=0.673, d2=0.680 g=0.751\n",
      ">5, 42/468, d1=0.673, d2=0.707 g=0.767\n",
      ">5, 43/468, d1=0.657, d2=0.695 g=0.733\n",
      ">5, 44/468, d1=0.645, d2=0.681 g=0.755\n",
      ">5, 45/468, d1=0.669, d2=0.684 g=0.765\n",
      ">5, 46/468, d1=0.663, d2=0.669 g=0.781\n",
      ">5, 47/468, d1=0.657, d2=0.638 g=0.804\n",
      ">5, 48/468, d1=0.651, d2=0.623 g=0.797\n",
      ">5, 49/468, d1=0.623, d2=0.644 g=0.789\n",
      ">5, 50/468, d1=0.633, d2=0.681 g=0.753\n",
      ">5, 51/468, d1=0.653, d2=0.778 g=0.729\n",
      ">5, 52/468, d1=0.639, d2=0.729 g=0.740\n",
      ">5, 53/468, d1=0.663, d2=0.715 g=0.728\n",
      ">5, 54/468, d1=0.650, d2=0.699 g=0.742\n",
      ">5, 55/468, d1=0.672, d2=0.676 g=0.776\n",
      ">5, 56/468, d1=0.686, d2=0.642 g=0.819\n",
      ">5, 57/468, d1=0.691, d2=0.614 g=0.816\n",
      ">5, 58/468, d1=0.690, d2=0.629 g=0.827\n",
      ">5, 59/468, d1=0.687, d2=0.603 g=0.815\n",
      ">5, 60/468, d1=0.719, d2=0.624 g=0.794\n",
      ">5, 61/468, d1=0.703, d2=0.665 g=0.771\n",
      ">5, 62/468, d1=0.678, d2=0.665 g=0.740\n",
      ">5, 63/468, d1=0.668, d2=0.713 g=0.732\n",
      ">5, 64/468, d1=0.691, d2=0.697 g=0.737\n",
      ">5, 65/468, d1=0.666, d2=0.690 g=0.784\n",
      ">5, 66/468, d1=0.672, d2=0.654 g=0.799\n",
      ">5, 67/468, d1=0.668, d2=0.622 g=0.823\n",
      ">5, 68/468, d1=0.673, d2=0.639 g=0.820\n",
      ">5, 69/468, d1=0.681, d2=0.664 g=0.779\n",
      ">5, 70/468, d1=0.679, d2=0.691 g=0.764\n",
      ">5, 71/468, d1=0.646, d2=0.708 g=0.729\n",
      ">5, 72/468, d1=0.676, d2=0.691 g=0.731\n",
      ">5, 73/468, d1=0.673, d2=0.695 g=0.733\n",
      ">5, 74/468, d1=0.677, d2=0.694 g=0.763\n",
      ">5, 75/468, d1=0.674, d2=0.663 g=0.794\n",
      ">5, 76/468, d1=0.706, d2=0.642 g=0.782\n",
      ">5, 77/468, d1=0.683, d2=0.621 g=0.796\n",
      ">5, 78/468, d1=0.665, d2=0.632 g=0.809\n",
      ">5, 79/468, d1=0.711, d2=0.612 g=0.821\n",
      ">5, 80/468, d1=0.702, d2=0.660 g=0.802\n",
      ">5, 81/468, d1=0.688, d2=0.642 g=0.795\n",
      ">5, 82/468, d1=0.709, d2=0.665 g=0.765\n",
      ">5, 83/468, d1=0.692, d2=0.661 g=0.759\n",
      ">5, 84/468, d1=0.711, d2=0.664 g=0.755\n",
      ">5, 85/468, d1=0.673, d2=0.675 g=0.769\n",
      ">5, 86/468, d1=0.667, d2=0.631 g=0.784\n",
      ">5, 87/468, d1=0.685, d2=0.648 g=0.796\n",
      ">5, 88/468, d1=0.670, d2=0.649 g=0.753\n",
      ">5, 89/468, d1=0.670, d2=0.682 g=0.760\n",
      ">5, 90/468, d1=0.631, d2=0.696 g=0.732\n",
      ">5, 91/468, d1=0.648, d2=0.688 g=0.727\n",
      ">5, 92/468, d1=0.670, d2=0.718 g=0.745\n",
      ">5, 93/468, d1=0.663, d2=0.689 g=0.764\n",
      ">5, 94/468, d1=0.676, d2=0.662 g=0.778\n",
      ">5, 95/468, d1=0.687, d2=0.651 g=0.799\n",
      ">5, 96/468, d1=0.680, d2=0.623 g=0.815\n",
      ">5, 97/468, d1=0.663, d2=0.625 g=0.822\n",
      ">5, 98/468, d1=0.698, d2=0.632 g=0.825\n",
      ">5, 99/468, d1=0.718, d2=0.639 g=0.793\n",
      ">5, 100/468, d1=0.685, d2=0.658 g=0.746\n",
      ">5, 101/468, d1=0.665, d2=0.675 g=0.730\n",
      ">5, 102/468, d1=0.673, d2=0.692 g=0.735\n",
      ">5, 103/468, d1=0.661, d2=0.657 g=0.746\n",
      ">5, 104/468, d1=0.655, d2=0.659 g=0.778\n",
      ">5, 105/468, d1=0.684, d2=0.649 g=0.786\n",
      ">5, 106/468, d1=0.688, d2=0.643 g=0.807\n",
      ">5, 107/468, d1=0.652, d2=0.626 g=0.801\n",
      ">5, 108/468, d1=0.641, d2=0.652 g=0.788\n",
      ">5, 109/468, d1=0.682, d2=0.672 g=0.765\n",
      ">5, 110/468, d1=0.637, d2=0.680 g=0.734\n",
      ">5, 111/468, d1=0.653, d2=0.700 g=0.754\n",
      ">5, 112/468, d1=0.682, d2=0.648 g=0.748\n",
      ">5, 113/468, d1=0.667, d2=0.685 g=0.759\n",
      ">5, 114/468, d1=0.699, d2=0.674 g=0.807\n",
      ">5, 115/468, d1=0.698, d2=0.617 g=0.851\n",
      ">5, 116/468, d1=0.691, d2=0.617 g=0.831\n",
      ">5, 117/468, d1=0.691, d2=0.606 g=0.846\n",
      ">5, 118/468, d1=0.697, d2=0.617 g=0.794\n",
      ">5, 119/468, d1=0.673, d2=0.675 g=0.749\n",
      ">5, 120/468, d1=0.675, d2=0.699 g=0.750\n",
      ">5, 121/468, d1=0.683, d2=0.701 g=0.733\n",
      ">5, 122/468, d1=0.651, d2=0.704 g=0.742\n",
      ">5, 123/468, d1=0.685, d2=0.676 g=0.768\n",
      ">5, 124/468, d1=0.669, d2=0.646 g=0.797\n",
      ">5, 125/468, d1=0.683, d2=0.612 g=0.830\n",
      ">5, 126/468, d1=0.682, d2=0.634 g=0.845\n",
      ">5, 127/468, d1=0.676, d2=0.659 g=0.825\n",
      ">5, 128/468, d1=0.656, d2=0.629 g=0.765\n",
      ">5, 129/468, d1=0.680, d2=0.692 g=0.737\n",
      ">5, 130/468, d1=0.638, d2=0.718 g=0.718\n",
      ">5, 131/468, d1=0.655, d2=0.719 g=0.698\n",
      ">5, 132/468, d1=0.637, d2=0.725 g=0.733\n",
      ">5, 133/468, d1=0.658, d2=0.721 g=0.739\n",
      ">5, 134/468, d1=0.665, d2=0.670 g=0.815\n",
      ">5, 135/468, d1=0.668, d2=0.625 g=0.842\n",
      ">5, 136/468, d1=0.708, d2=0.605 g=0.861\n",
      ">5, 137/468, d1=0.687, d2=0.592 g=0.881\n",
      ">5, 138/468, d1=0.669, d2=0.622 g=0.821\n",
      ">5, 139/468, d1=0.710, d2=0.642 g=0.794\n",
      ">5, 140/468, d1=0.693, d2=0.665 g=0.750\n",
      ">5, 141/468, d1=0.687, d2=0.720 g=0.754\n",
      ">5, 142/468, d1=0.669, d2=0.706 g=0.731\n",
      ">5, 143/468, d1=0.672, d2=0.702 g=0.726\n",
      ">5, 144/468, d1=0.657, d2=0.679 g=0.749\n",
      ">5, 145/468, d1=0.656, d2=0.643 g=0.779\n",
      ">5, 146/468, d1=0.633, d2=0.670 g=0.809\n",
      ">5, 147/468, d1=0.628, d2=0.624 g=0.788\n",
      ">5, 148/468, d1=0.617, d2=0.646 g=0.777\n",
      ">5, 149/468, d1=0.647, d2=0.686 g=0.772\n",
      ">5, 150/468, d1=0.650, d2=0.726 g=0.736\n",
      ">5, 151/468, d1=0.633, d2=0.733 g=0.710\n",
      ">5, 152/468, d1=0.657, d2=0.769 g=0.686\n",
      ">5, 153/468, d1=0.643, d2=0.699 g=0.733\n",
      ">5, 154/468, d1=0.675, d2=0.683 g=0.741\n",
      ">5, 155/468, d1=0.683, d2=0.667 g=0.794\n",
      ">5, 156/468, d1=0.650, d2=0.640 g=0.788\n",
      ">5, 157/468, d1=0.696, d2=0.620 g=0.817\n",
      ">5, 158/468, d1=0.686, d2=0.609 g=0.828\n",
      ">5, 159/468, d1=0.693, d2=0.631 g=0.829\n",
      ">5, 160/468, d1=0.677, d2=0.645 g=0.817\n",
      ">5, 161/468, d1=0.690, d2=0.665 g=0.758\n",
      ">5, 162/468, d1=0.696, d2=0.682 g=0.750\n",
      ">5, 163/468, d1=0.680, d2=0.666 g=0.749\n",
      ">5, 164/468, d1=0.660, d2=0.702 g=0.719\n",
      ">5, 165/468, d1=0.700, d2=0.693 g=0.741\n",
      ">5, 166/468, d1=0.672, d2=0.661 g=0.770\n",
      ">5, 167/468, d1=0.668, d2=0.640 g=0.776\n",
      ">5, 168/468, d1=0.674, d2=0.631 g=0.792\n",
      ">5, 169/468, d1=0.644, d2=0.657 g=0.809\n",
      ">5, 170/468, d1=0.650, d2=0.650 g=0.786\n",
      ">5, 171/468, d1=0.663, d2=0.667 g=0.759\n",
      ">5, 172/468, d1=0.616, d2=0.683 g=0.731\n",
      ">5, 173/468, d1=0.629, d2=0.698 g=0.716\n",
      ">5, 174/468, d1=0.657, d2=0.774 g=0.725\n",
      ">5, 175/468, d1=0.683, d2=0.675 g=0.739\n",
      ">5, 176/468, d1=0.641, d2=0.679 g=0.778\n",
      ">5, 177/468, d1=0.672, d2=0.650 g=0.794\n",
      ">5, 178/468, d1=0.651, d2=0.621 g=0.803\n",
      ">5, 179/468, d1=0.698, d2=0.632 g=0.824\n",
      ">5, 180/468, d1=0.692, d2=0.650 g=0.808\n",
      ">5, 181/468, d1=0.679, d2=0.648 g=0.782\n",
      ">5, 182/468, d1=0.700, d2=0.652 g=0.767\n",
      ">5, 183/468, d1=0.696, d2=0.681 g=0.749\n",
      ">5, 184/468, d1=0.696, d2=0.710 g=0.744\n",
      ">5, 185/468, d1=0.669, d2=0.703 g=0.736\n",
      ">5, 186/468, d1=0.668, d2=0.685 g=0.752\n",
      ">5, 187/468, d1=0.650, d2=0.680 g=0.739\n",
      ">5, 188/468, d1=0.646, d2=0.639 g=0.777\n",
      ">5, 189/468, d1=0.631, d2=0.639 g=0.817\n",
      ">5, 190/468, d1=0.651, d2=0.631 g=0.792\n",
      ">5, 191/468, d1=0.643, d2=0.659 g=0.769\n",
      ">5, 192/468, d1=0.661, d2=0.680 g=0.750\n",
      ">5, 193/468, d1=0.645, d2=0.734 g=0.726\n",
      ">5, 194/468, d1=0.631, d2=0.705 g=0.707\n",
      ">5, 195/468, d1=0.642, d2=0.733 g=0.727\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">5, 196/468, d1=0.620, d2=0.731 g=0.772\n",
      ">5, 197/468, d1=0.678, d2=0.679 g=0.782\n",
      ">5, 198/468, d1=0.659, d2=0.636 g=0.817\n",
      ">5, 199/468, d1=0.674, d2=0.648 g=0.855\n",
      ">5, 200/468, d1=0.703, d2=0.603 g=0.838\n",
      ">5, 201/468, d1=0.715, d2=0.648 g=0.805\n",
      ">5, 202/468, d1=0.683, d2=0.686 g=0.788\n",
      ">5, 203/468, d1=0.663, d2=0.661 g=0.757\n",
      ">5, 204/468, d1=0.690, d2=0.657 g=0.748\n",
      ">5, 205/468, d1=0.669, d2=0.712 g=0.732\n",
      ">5, 206/468, d1=0.692, d2=0.709 g=0.709\n",
      ">5, 207/468, d1=0.664, d2=0.699 g=0.715\n",
      ">5, 208/468, d1=0.671, d2=0.688 g=0.745\n",
      ">5, 209/468, d1=0.657, d2=0.654 g=0.762\n",
      ">5, 210/468, d1=0.657, d2=0.641 g=0.794\n",
      ">5, 211/468, d1=0.648, d2=0.645 g=0.810\n",
      ">5, 212/468, d1=0.607, d2=0.656 g=0.812\n",
      ">5, 213/468, d1=0.671, d2=0.647 g=0.798\n",
      ">5, 214/468, d1=0.672, d2=0.683 g=0.752\n",
      ">5, 215/468, d1=0.635, d2=0.708 g=0.723\n",
      ">5, 216/468, d1=0.661, d2=0.713 g=0.718\n",
      ">5, 217/468, d1=0.660, d2=0.729 g=0.746\n",
      ">5, 218/468, d1=0.691, d2=0.681 g=0.761\n",
      ">5, 219/468, d1=0.681, d2=0.669 g=0.785\n",
      ">5, 220/468, d1=0.668, d2=0.638 g=0.816\n",
      ">5, 221/468, d1=0.675, d2=0.622 g=0.839\n",
      ">5, 222/468, d1=0.680, d2=0.643 g=0.820\n",
      ">5, 223/468, d1=0.699, d2=0.626 g=0.803\n",
      ">5, 224/468, d1=0.672, d2=0.651 g=0.785\n",
      ">5, 225/468, d1=0.648, d2=0.661 g=0.755\n",
      ">5, 226/468, d1=0.676, d2=0.688 g=0.748\n",
      ">5, 227/468, d1=0.676, d2=0.685 g=0.739\n",
      ">5, 228/468, d1=0.673, d2=0.692 g=0.756\n",
      ">5, 229/468, d1=0.678, d2=0.684 g=0.770\n",
      ">5, 230/468, d1=0.662, d2=0.660 g=0.778\n",
      ">5, 231/468, d1=0.644, d2=0.642 g=0.784\n",
      ">5, 232/468, d1=0.672, d2=0.645 g=0.778\n",
      ">5, 233/468, d1=0.672, d2=0.660 g=0.779\n",
      ">5, 234/468, d1=0.681, d2=0.676 g=0.772\n",
      ">5, 235/468, d1=0.651, d2=0.683 g=0.752\n",
      ">5, 236/468, d1=0.677, d2=0.681 g=0.766\n",
      ">5, 237/468, d1=0.670, d2=0.672 g=0.774\n",
      ">5, 238/468, d1=0.667, d2=0.648 g=0.749\n",
      ">5, 239/468, d1=0.678, d2=0.664 g=0.787\n",
      ">5, 240/468, d1=0.668, d2=0.658 g=0.808\n",
      ">5, 241/468, d1=0.699, d2=0.637 g=0.850\n",
      ">5, 242/468, d1=0.667, d2=0.628 g=0.826\n",
      ">5, 243/468, d1=0.688, d2=0.626 g=0.806\n",
      ">5, 244/468, d1=0.685, d2=0.622 g=0.789\n",
      ">5, 245/468, d1=0.687, d2=0.659 g=0.784\n",
      ">5, 246/468, d1=0.713, d2=0.634 g=0.804\n",
      ">5, 247/468, d1=0.661, d2=0.648 g=0.789\n",
      ">5, 248/468, d1=0.704, d2=0.662 g=0.778\n",
      ">5, 249/468, d1=0.664, d2=0.659 g=0.770\n",
      ">5, 250/468, d1=0.650, d2=0.647 g=0.779\n",
      ">5, 251/468, d1=0.649, d2=0.665 g=0.779\n",
      ">5, 252/468, d1=0.645, d2=0.649 g=0.775\n",
      ">5, 253/468, d1=0.679, d2=0.688 g=0.753\n",
      ">5, 254/468, d1=0.678, d2=0.666 g=0.764\n",
      ">5, 255/468, d1=0.679, d2=0.675 g=0.754\n",
      ">5, 256/468, d1=0.662, d2=0.654 g=0.767\n",
      ">5, 257/468, d1=0.651, d2=0.686 g=0.755\n",
      ">5, 258/468, d1=0.648, d2=0.641 g=0.793\n",
      ">5, 259/468, d1=0.673, d2=0.655 g=0.779\n",
      ">5, 260/468, d1=0.661, d2=0.654 g=0.790\n",
      ">5, 261/468, d1=0.689, d2=0.662 g=0.768\n",
      ">5, 262/468, d1=0.689, d2=0.652 g=0.762\n",
      ">5, 263/468, d1=0.677, d2=0.668 g=0.774\n",
      ">5, 264/468, d1=0.651, d2=0.642 g=0.766\n",
      ">5, 265/468, d1=0.656, d2=0.676 g=0.782\n",
      ">5, 266/468, d1=0.696, d2=0.655 g=0.769\n",
      ">5, 267/468, d1=0.689, d2=0.648 g=0.777\n",
      ">5, 268/468, d1=0.682, d2=0.650 g=0.774\n",
      ">5, 269/468, d1=0.663, d2=0.654 g=0.780\n",
      ">5, 270/468, d1=0.688, d2=0.657 g=0.784\n",
      ">5, 271/468, d1=0.648, d2=0.661 g=0.775\n",
      ">5, 272/468, d1=0.685, d2=0.650 g=0.764\n",
      ">5, 273/468, d1=0.670, d2=0.652 g=0.766\n",
      ">5, 274/468, d1=0.674, d2=0.672 g=0.787\n",
      ">5, 275/468, d1=0.665, d2=0.671 g=0.765\n",
      ">5, 276/468, d1=0.659, d2=0.669 g=0.773\n",
      ">5, 277/468, d1=0.665, d2=0.653 g=0.778\n",
      ">5, 278/468, d1=0.676, d2=0.641 g=0.770\n",
      ">5, 279/468, d1=0.674, d2=0.675 g=0.789\n",
      ">5, 280/468, d1=0.687, d2=0.682 g=0.776\n",
      ">5, 281/468, d1=0.663, d2=0.655 g=0.772\n",
      ">5, 282/468, d1=0.681, d2=0.653 g=0.752\n",
      ">5, 283/468, d1=0.675, d2=0.682 g=0.761\n",
      ">5, 284/468, d1=0.701, d2=0.697 g=0.751\n",
      ">5, 285/468, d1=0.678, d2=0.675 g=0.757\n",
      ">5, 286/468, d1=0.664, d2=0.662 g=0.770\n",
      ">5, 287/468, d1=0.658, d2=0.662 g=0.796\n",
      ">5, 288/468, d1=0.657, d2=0.642 g=0.766\n",
      ">5, 289/468, d1=0.677, d2=0.674 g=0.762\n",
      ">5, 290/468, d1=0.666, d2=0.668 g=0.775\n",
      ">5, 291/468, d1=0.650, d2=0.678 g=0.791\n",
      ">5, 292/468, d1=0.697, d2=0.670 g=0.771\n",
      ">5, 293/468, d1=0.657, d2=0.657 g=0.781\n",
      ">5, 294/468, d1=0.678, d2=0.659 g=0.785\n",
      ">5, 295/468, d1=0.700, d2=0.647 g=0.802\n",
      ">5, 296/468, d1=0.677, d2=0.639 g=0.790\n",
      ">5, 297/468, d1=0.675, d2=0.657 g=0.794\n",
      ">5, 298/468, d1=0.680, d2=0.661 g=0.776\n",
      ">5, 299/468, d1=0.692, d2=0.674 g=0.774\n",
      ">5, 300/468, d1=0.672, d2=0.641 g=0.778\n",
      ">5, 301/468, d1=0.678, d2=0.659 g=0.773\n",
      ">5, 302/468, d1=0.654, d2=0.667 g=0.753\n",
      ">5, 303/468, d1=0.675, d2=0.654 g=0.763\n",
      ">5, 304/468, d1=0.678, d2=0.659 g=0.762\n",
      ">5, 305/468, d1=0.665, d2=0.657 g=0.766\n",
      ">5, 306/468, d1=0.675, d2=0.667 g=0.773\n",
      ">5, 307/468, d1=0.644, d2=0.679 g=0.766\n",
      ">5, 308/468, d1=0.649, d2=0.673 g=0.754\n",
      ">5, 309/468, d1=0.649, d2=0.669 g=0.771\n",
      ">5, 310/468, d1=0.653, d2=0.664 g=0.761\n",
      ">5, 311/468, d1=0.629, d2=0.653 g=0.779\n",
      ">5, 312/468, d1=0.662, d2=0.669 g=0.764\n",
      ">5, 313/468, d1=0.669, d2=0.645 g=0.771\n",
      ">5, 314/468, d1=0.686, d2=0.661 g=0.780\n",
      ">5, 315/468, d1=0.670, d2=0.652 g=0.801\n",
      ">5, 316/468, d1=0.680, d2=0.645 g=0.791\n",
      ">5, 317/468, d1=0.680, d2=0.673 g=0.783\n",
      ">5, 318/468, d1=0.692, d2=0.660 g=0.794\n",
      ">5, 319/468, d1=0.702, d2=0.656 g=0.769\n",
      ">5, 320/468, d1=0.642, d2=0.654 g=0.741\n",
      ">5, 321/468, d1=0.696, d2=0.656 g=0.738\n",
      ">5, 322/468, d1=0.664, d2=0.659 g=0.764\n",
      ">5, 323/468, d1=0.648, d2=0.672 g=0.754\n",
      ">5, 324/468, d1=0.675, d2=0.660 g=0.759\n",
      ">5, 325/468, d1=0.670, d2=0.687 g=0.776\n",
      ">5, 326/468, d1=0.680, d2=0.686 g=0.769\n",
      ">5, 327/468, d1=0.664, d2=0.681 g=0.763\n",
      ">5, 328/468, d1=0.666, d2=0.670 g=0.761\n",
      ">5, 329/468, d1=0.631, d2=0.657 g=0.754\n",
      ">5, 330/468, d1=0.660, d2=0.658 g=0.770\n",
      ">5, 331/468, d1=0.688, d2=0.659 g=0.784\n",
      ">5, 332/468, d1=0.675, d2=0.635 g=0.784\n",
      ">5, 333/468, d1=0.670, d2=0.631 g=0.783\n",
      ">5, 334/468, d1=0.679, d2=0.671 g=0.761\n",
      ">5, 335/468, d1=0.676, d2=0.646 g=0.769\n",
      ">5, 336/468, d1=0.657, d2=0.671 g=0.751\n",
      ">5, 337/468, d1=0.664, d2=0.661 g=0.779\n",
      ">5, 338/468, d1=0.666, d2=0.660 g=0.765\n",
      ">5, 339/468, d1=0.647, d2=0.642 g=0.770\n",
      ">5, 340/468, d1=0.670, d2=0.679 g=0.781\n",
      ">5, 341/468, d1=0.678, d2=0.681 g=0.764\n",
      ">5, 342/468, d1=0.645, d2=0.674 g=0.755\n",
      ">5, 343/468, d1=0.655, d2=0.657 g=0.757\n",
      ">5, 344/468, d1=0.652, d2=0.680 g=0.736\n",
      ">5, 345/468, d1=0.658, d2=0.669 g=0.767\n",
      ">5, 346/468, d1=0.654, d2=0.676 g=0.747\n",
      ">5, 347/468, d1=0.650, d2=0.651 g=0.776\n",
      ">5, 348/468, d1=0.651, d2=0.657 g=0.763\n",
      ">5, 349/468, d1=0.677, d2=0.673 g=0.768\n",
      ">5, 350/468, d1=0.674, d2=0.657 g=0.761\n",
      ">5, 351/468, d1=0.674, d2=0.662 g=0.751\n",
      ">5, 352/468, d1=0.640, d2=0.694 g=0.766\n",
      ">5, 353/468, d1=0.670, d2=0.664 g=0.761\n",
      ">5, 354/468, d1=0.657, d2=0.657 g=0.757\n",
      ">5, 355/468, d1=0.660, d2=0.662 g=0.750\n",
      ">5, 356/468, d1=0.673, d2=0.671 g=0.766\n",
      ">5, 357/468, d1=0.661, d2=0.661 g=0.761\n",
      ">5, 358/468, d1=0.654, d2=0.669 g=0.750\n",
      ">5, 359/468, d1=0.665, d2=0.642 g=0.758\n",
      ">5, 360/468, d1=0.634, d2=0.631 g=0.759\n",
      ">5, 361/468, d1=0.645, d2=0.663 g=0.751\n",
      ">5, 362/468, d1=0.656, d2=0.654 g=0.748\n",
      ">5, 363/468, d1=0.667, d2=0.710 g=0.749\n",
      ">5, 364/468, d1=0.674, d2=0.684 g=0.739\n",
      ">5, 365/468, d1=0.656, d2=0.656 g=0.774\n",
      ">5, 366/468, d1=0.658, d2=0.664 g=0.815\n",
      ">5, 367/468, d1=0.679, d2=0.655 g=0.810\n",
      ">5, 368/468, d1=0.669, d2=0.620 g=0.805\n",
      ">5, 369/468, d1=0.683, d2=0.620 g=0.834\n",
      ">5, 370/468, d1=0.702, d2=0.652 g=0.829\n",
      ">5, 371/468, d1=0.738, d2=0.643 g=0.788\n",
      ">5, 372/468, d1=0.702, d2=0.653 g=0.784\n",
      ">5, 373/468, d1=0.701, d2=0.688 g=0.775\n",
      ">5, 374/468, d1=0.680, d2=0.669 g=0.795\n",
      ">5, 375/468, d1=0.693, d2=0.641 g=0.821\n",
      ">5, 376/468, d1=0.690, d2=0.630 g=0.853\n",
      ">5, 377/468, d1=0.698, d2=0.628 g=0.823\n",
      ">5, 378/468, d1=0.687, d2=0.633 g=0.812\n",
      ">5, 379/468, d1=0.690, d2=0.638 g=0.799\n",
      ">5, 380/468, d1=0.639, d2=0.672 g=0.769\n",
      ">5, 381/468, d1=0.692, d2=0.734 g=0.777\n",
      ">5, 382/468, d1=0.683, d2=0.675 g=0.749\n",
      ">5, 383/468, d1=0.666, d2=0.678 g=0.811\n",
      ">5, 384/468, d1=0.657, d2=0.648 g=0.849\n",
      ">5, 385/468, d1=0.687, d2=0.614 g=0.870\n",
      ">5, 386/468, d1=0.687, d2=0.605 g=0.845\n",
      ">5, 387/468, d1=0.672, d2=0.617 g=0.847\n",
      ">5, 388/468, d1=0.716, d2=0.629 g=0.816\n",
      ">5, 389/468, d1=0.696, d2=0.662 g=0.803\n",
      ">5, 390/468, d1=0.707, d2=0.694 g=0.751\n",
      ">5, 391/468, d1=0.685, d2=0.708 g=0.730\n",
      ">5, 392/468, d1=0.674, d2=0.702 g=0.743\n",
      ">5, 393/468, d1=0.638, d2=0.668 g=0.772\n",
      ">5, 394/468, d1=0.690, d2=0.655 g=0.810\n",
      ">5, 395/468, d1=0.638, d2=0.676 g=0.815\n",
      ">5, 396/468, d1=0.630, d2=0.650 g=0.796\n",
      ">5, 397/468, d1=0.649, d2=0.706 g=0.754\n",
      ">5, 398/468, d1=0.702, d2=0.717 g=0.778\n",
      ">5, 399/468, d1=0.646, d2=0.779 g=0.723\n",
      ">5, 400/468, d1=0.639, d2=0.734 g=0.723\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">5, 401/468, d1=0.644, d2=0.673 g=0.787\n",
      ">5, 402/468, d1=0.677, d2=0.646 g=0.807\n",
      ">5, 403/468, d1=0.704, d2=0.617 g=0.833\n",
      ">5, 404/468, d1=0.682, d2=0.578 g=0.866\n",
      ">5, 405/468, d1=0.681, d2=0.611 g=0.857\n",
      ">5, 406/468, d1=0.719, d2=0.612 g=0.854\n",
      ">5, 407/468, d1=0.693, d2=0.665 g=0.772\n",
      ">5, 408/468, d1=0.662, d2=0.660 g=0.747\n",
      ">5, 409/468, d1=0.626, d2=0.703 g=0.731\n",
      ">5, 410/468, d1=0.673, d2=0.691 g=0.746\n",
      ">5, 411/468, d1=0.673, d2=0.678 g=0.761\n",
      ">5, 412/468, d1=0.662, d2=0.630 g=0.782\n",
      ">5, 413/468, d1=0.641, d2=0.629 g=0.855\n",
      ">5, 414/468, d1=0.630, d2=0.579 g=0.845\n",
      ">5, 415/468, d1=0.629, d2=0.626 g=0.818\n",
      ">5, 416/468, d1=0.655, d2=0.663 g=0.780\n",
      ">5, 417/468, d1=0.637, d2=0.686 g=0.718\n",
      ">5, 418/468, d1=0.677, d2=0.705 g=0.706\n",
      ">5, 419/468, d1=0.621, d2=0.754 g=0.690\n",
      ">5, 420/468, d1=0.648, d2=0.726 g=0.730\n",
      ">5, 421/468, d1=0.658, d2=0.663 g=0.775\n",
      ">5, 422/468, d1=0.660, d2=0.617 g=0.850\n",
      ">5, 423/468, d1=0.672, d2=0.606 g=0.880\n",
      ">5, 424/468, d1=0.700, d2=0.584 g=0.887\n",
      ">5, 425/468, d1=0.642, d2=0.607 g=0.888\n",
      ">5, 426/468, d1=0.682, d2=0.612 g=0.824\n",
      ">5, 427/468, d1=0.684, d2=0.665 g=0.795\n",
      ">5, 428/468, d1=0.672, d2=0.664 g=0.766\n",
      ">5, 429/468, d1=0.689, d2=0.711 g=0.725\n",
      ">5, 430/468, d1=0.704, d2=0.741 g=0.725\n",
      ">5, 431/468, d1=0.652, d2=0.677 g=0.774\n",
      ">5, 432/468, d1=0.654, d2=0.633 g=0.829\n",
      ">5, 433/468, d1=0.645, d2=0.595 g=0.852\n",
      ">5, 434/468, d1=0.640, d2=0.629 g=0.860\n",
      ">5, 435/468, d1=0.637, d2=0.598 g=0.824\n",
      ">5, 436/468, d1=0.599, d2=0.691 g=0.768\n",
      ">5, 437/468, d1=0.690, d2=0.729 g=0.739\n",
      ">5, 438/468, d1=0.610, d2=0.730 g=0.718\n",
      ">5, 439/468, d1=0.667, d2=0.701 g=0.751\n",
      ">5, 440/468, d1=0.692, d2=0.713 g=0.789\n",
      ">5, 441/468, d1=0.641, d2=0.671 g=0.818\n",
      ">5, 442/468, d1=0.678, d2=0.625 g=0.825\n",
      ">5, 443/468, d1=0.681, d2=0.627 g=0.857\n",
      ">5, 444/468, d1=0.694, d2=0.605 g=0.858\n",
      ">5, 445/468, d1=0.680, d2=0.604 g=0.834\n",
      ">5, 446/468, d1=0.661, d2=0.653 g=0.820\n",
      ">5, 447/468, d1=0.725, d2=0.691 g=0.733\n",
      ">5, 448/468, d1=0.672, d2=0.741 g=0.715\n",
      ">5, 449/468, d1=0.677, d2=0.728 g=0.716\n",
      ">5, 450/468, d1=0.687, d2=0.719 g=0.742\n",
      ">5, 451/468, d1=0.663, d2=0.678 g=0.771\n",
      ">5, 452/468, d1=0.670, d2=0.639 g=0.827\n",
      ">5, 453/468, d1=0.699, d2=0.627 g=0.826\n",
      ">5, 454/468, d1=0.655, d2=0.618 g=0.840\n",
      ">5, 455/468, d1=0.652, d2=0.620 g=0.857\n",
      ">5, 456/468, d1=0.671, d2=0.636 g=0.834\n",
      ">5, 457/468, d1=0.652, d2=0.663 g=0.763\n",
      ">5, 458/468, d1=0.647, d2=0.724 g=0.743\n",
      ">5, 459/468, d1=0.613, d2=0.716 g=0.775\n",
      ">5, 460/468, d1=0.643, d2=0.703 g=0.773\n",
      ">5, 461/468, d1=0.698, d2=0.689 g=0.772\n",
      ">5, 462/468, d1=0.679, d2=0.673 g=0.803\n",
      ">5, 463/468, d1=0.675, d2=0.612 g=0.854\n",
      ">5, 464/468, d1=0.667, d2=0.581 g=0.880\n",
      ">5, 465/468, d1=0.680, d2=0.609 g=0.857\n",
      ">5, 466/468, d1=0.684, d2=0.596 g=0.852\n",
      ">5, 467/468, d1=0.678, d2=0.638 g=0.808\n",
      ">5, 468/468, d1=0.684, d2=0.695 g=0.759\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# train the generator and discriminator\n",
    "def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=5, n_batch=128):\n",
    "\tbat_per_epo = int(dataset[0].shape[0] / n_batch)\n",
    "\thalf_batch = int(n_batch / 2)\n",
    "\t# manually enumerate epochs\n",
    "\tfor i in range(n_epochs):\n",
    "\t\t# enumerate batches over the training set\n",
    "\t\tfor j in range(bat_per_epo):\n",
    "\t\t\t# get randomly selected 'real' samples\n",
    "\t\t\t[X_real, labels_real], y_real = generate_real_samples(dataset, half_batch)\n",
    "\t\t\t# update discriminator model weights\n",
    "\t\t\td_loss1, _ = d_model.train_on_batch([X_real, labels_real], y_real)\n",
    "\t\t\t# generate 'fake' examples\n",
    "\t\t\t[X_fake, labels], y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
    "\t\t\t# update discriminator model weights\n",
    "\t\t\td_loss2, _ = d_model.train_on_batch([X_fake, labels], y_fake)\n",
    "\t\t\t# prepare points in latent space as input for the generator\n",
    "\t\t\t[z_input, labels_input] = generate_latent_points(latent_dim, n_batch)\n",
    "\t\t\t# create inverted labels for the fake samples\n",
    "\t\t\ty_gan = ones((n_batch, 1))\n",
    "\t\t\t# update the generator via the discriminator's error\n",
    "\t\t\tg_loss = gan_model.train_on_batch([z_input, labels_input], y_gan)\n",
    "\t\t\t# summarize loss on this batch\n",
    "\t\t\tprint('>%d, %d/%d, d1=%.3f, d2=%.3f g=%.3f' %\n",
    "\t\t\t\t(i+1, j+1, bat_per_epo, d_loss1, d_loss2, g_loss))\n",
    "\t# save the generator model\n",
    "\tg_model.save('cgan_generator.h5')\n",
    "\n",
    "# size of the latent space\n",
    "latent_dim = 100\n",
    "# create the discriminator\n",
    "d_model = define_discriminator()\n",
    "# create the generator\n",
    "g_model = define_generator(latent_dim)\n",
    "# create the gan\n",
    "gan_model = define_gan(g_model, d_model)\n",
    "# load image data\n",
    "dataset = load_real_samples()\n",
    "# train model\n",
    "train(g_model, d_model, gan_model, dataset, latent_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example of loading the generator model and generating images\n",
    "from numpy import asarray\n",
    "from numpy.random import randn\n",
    "from numpy.random import randint\n",
    "from keras.models import load_model\n",
    "from matplotlib import pyplot\n",
    "\n",
    "# generate points in latent space as input for the generator\n",
    "def generate_latent_points(latent_dim, n_samples, n_classes=10):\n",
    "\t# generate points in the latent space\n",
    "\tx_input = randn(latent_dim * n_samples)\n",
    "\t# reshape into a batch of inputs for the network\n",
    "\tz_input = x_input.reshape(n_samples, latent_dim)\n",
    "\t# generate labels\n",
    "\tlabels = randint(0, n_classes, n_samples)\n",
    "\treturn [z_input, labels]\n",
    "\n",
    "# create and save a plot of generated images\n",
    "def save_plot(examples, n):\n",
    "\t# plot images\n",
    "\tfor i in range(n * n):\n",
    "\t\t# define subplot\n",
    "\t\tpyplot.subplot(n, n, 1 + i)\n",
    "\t\t# turn off axis\n",
    "\t\tpyplot.axis('off')\n",
    "\t\t# plot raw pixel data\n",
    "\t\tpyplot.imshow(examples[i, :, :, 0], cmap='gray_r')\n",
    "\tpyplot.show()\n",
    "\n",
    "# load model\n",
    "model = load_model('cgan_generator.h5')\n",
    "# generate images\n",
    "latent_points, labels = generate_latent_points(100, 100)\n",
    "# specify labels\n",
    "labels = asarray([x for _ in range(10) for x in range(10)])\n",
    "# generate images\n",
    "X  = model.predict([latent_points, labels])\n",
    "# scale from [-1,1] to [0,1]\n",
    "X = (X + 1) / 2.0\n",
    "# plot the result\n",
    "save_plot(X, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_model.load_weights('cgan_generator.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example of loading the generator model and generating images\n",
    "from numpy import asarray\n",
    "from numpy.random import randn\n",
    "from numpy.random import randint\n",
    "from keras.models import load_model\n",
    "from matplotlib import pyplot\n",
    "\n",
    "# generate points in latent space as input for the generator\n",
    "def generate_latent_points(latent_dim, n_samples, n_classes=10):\n",
    "\t# generate points in the latent space\n",
    "\tx_input = randn(latent_dim * n_samples)\n",
    "\t# reshape into a batch of inputs for the network\n",
    "\tz_input = x_input.reshape(n_samples, latent_dim)\n",
    "\t# generate labels\n",
    "\tlabels = randint(0, n_classes, n_samples)\n",
    "\treturn [z_input, labels]\n",
    "\n",
    "# create and save a plot of generated images\n",
    "def save_plot(examples, n):\n",
    "\t# plot images\n",
    "\tfor i in range(n * n):\n",
    "\t\t# define subplot\n",
    "\t\tpyplot.subplot(n, n, 1 + i)\n",
    "\t\t# turn off axis\n",
    "\t\tpyplot.axis('off')\n",
    "\t\t# plot raw pixel data\n",
    "\t\tpyplot.imshow(examples[i, :, :, 0], cmap='gray_r')\n",
    "\tpyplot.show()\n",
    "\n",
    "# load model\n",
    "#model = load_model('cgan_generator.h5')\n",
    "model= g_model\n",
    "# generate images\n",
    "latent_points, labels = generate_latent_points(100, 100)\n",
    "# specify labels\n",
    "labels = asarray([x for _ in range(10) for x in range(10)])\n",
    "# generate images\n",
    "X  = model.predict([latent_points, labels])\n",
    "# scale from [-1,1] to [0,1]\n",
    "X = (X + 1) / 2.0\n",
    "# plot the result\n",
    "save_plot(X, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_points.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1,\n",
       "       2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3,\n",
       "       4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5,\n",
       "       6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7,\n",
       "       8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 100)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=randn(10*100*5)\n",
    "a=a.reshape(50,100)\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=randint(0, 5, len(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=b.reshape(50,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=g_model.predict([a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scale from [-1,1] to [0,1]\n",
    "p = (p + 1) / 2.0\n",
    "# plot the result\n",
    "save_plot(p, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}