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train_average.py
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train_average.py
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#!/usr/bin/python3
#
import numpy as np
import math
import random
import rbm
import sys
import pickle
import loading_module as ld
if len(sys.argv) < 5:
print("usage train data pickle width nrbm")
sys.exit(-1)
training_data = ld.data_module(sys.argv[1])
number_of_depends = training_data.ncolumns -1
number = training_data.nrows
print(number_of_depends)
print(number)
width = int(sys.argv[3])
n_rbm = int(sys.argv[4])
print(width)
print(n_rbm)
#n_rbm = 3
#n_rbm = 5
#n_rbm = 7
#n_rbm = 11
#n_rbm = 21
#n_rbm = width+1
#the_rbm = rbm.rbm( width-number_of_depends, n_rbm) #guessed parameters for the number of rbms
# change is due to the new form of data manager.
# width is the number of data points. Each datapoint is number_of_depends wide
the_rbm = rbm.rbm( width*number_of_depends, n_rbm) #guessed parameters for the number of rbms
the_rbm.its_symmetric()
the_rbm.add_multi_fuzzy(number_of_depends,-2., 2., 101)
print("training starts")
# initialize to more than the first n points
for i in range(0,n_rbm):
# j = int(i * number/n_rbm)
j = int(number*random.random()+0.5)
point = training_data.a_specific_data_point(j,width)
print(i, point)
the_rbm.initial_values( i, point[0])
# p = np.zeros(width-1)
# for j in range(0, width-1):
# p[j] = random.random()-0.5
# the_rbm.initial_values( i, p)
for j in range(0,10):
training_data.reset_data_index()
for i in range(0,number-width):
point = training_data.a_data_point(width)
the_rbm.train_multi_fuzzy(point[0],0.1, 1.0, point[1])
for i in range(0, n_rbm): # check that I've been trained
if the_rbm.seen_multi_data(i) == 0:
k = int(number*random.random()+0.5)
point = training_data.a_specific_data_point(k,width)
the_rbm.initial_values( i, point[0])
the_rbm.make_multi_cumulative()
print("training stops")
save_set = open( sys.argv[2], 'wb')
x = pickle.dumps( the_rbm)
save_set.write(x)
save_set.close()
# testing
for i in range(0, number):
point = training_data.a_data_point(width)
x = the_rbm.random_multi_choice( point[0], random.random())
print( str(point[0])+' '+str(point[1])+ ' ' + str(x))
training_data.set_up_average_prediction()
for k in range(0,21):
training_data.set_up_prediction(width)
for i in range(width, number):
point = training_data.a_prediction_data_point(width)
# x = the_rbm.random_multi_choice( point[0], random.random())
x = the_rbm.random_multi_independent_choice( point[0], random.random)
training_data.add_prediction(x,i)
training_data.add_average_prediction()
x = training_data.get_average_prediction()
x.to_csv("average_21.csv")
"""
# trial run
run = np.zeros(width-number_of_depends)
dataset.reset()
point = training_data.a_data_point()
for i in range(0,width-number_of_depends):
run[i] = (point[0])[i]
# run[i] = 1.
print(run[i])
window_size = int((width - number_of_depends)/number_of_depends)
for i in range(0,number):
point = dataset.a_point()
x = the_rbm.random_multi_choice( run, random.random())
print(str( (point[0])[0]) + ' , '+ str(x))
for k in range(0, number_of_depends):
for j in range(k*window_size+1,k*window_size+window_size):
run[j-1] = run[j]
run[k*window_size + window_size -1] = x[k]
# print(run)
"""