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regularised_linear&gaussian_basis_functions
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regularised_linear&gaussian_basis_functions
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#################### This cell is for all the methods and data import ################################
import os
import sklearn.model_selection
import pandas
from numpy.linalg import inv # matrix inverse
import random # for seeding and random no generation
from itertools import chain # for unlisting
import matplotlib.pyplot as matplot
from numpy import linalg
import numpy
# stratification
def folds_stratify(nSample, seed, kFolds): # this return kfold stratification
random.seed(seed)
foldSize = round(nSample / kFolds, 0)
randomList = list(range(0, nSample))
random.shuffle(randomList)
stra = []
for k in range(0, kFolds):
strt = int(k * foldSize)
end = int((k + 1) * foldSize)
if k == (kFolds - 1):
end = nSample
stra.append(list(randomList)[strt:end])
return stra
# end def folds_stratify
# linear basis function
def W_lbf(trainX, noutputs, lamda): # linear basis function# lamda = reqularization coefficient
nFeature = trainX.shape[1] - noutputs
trainX_p = trainX[:, 0:nFeature]
trainX_p = pandas.DataFrame(trainX_p)
X = pandas.concat([trainX_p[0], trainX_p], axis=1) # Adding one column to X
X.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
X = numpy.asarray(X)
phi = X # in linear basis function #ϕ
y = trainX[:, nFeature:nFeature + noutputs] # the last noutputs columns in trainX
phi_trans = phi.transpose() # phi transpose
phi_trans_phi = numpy.dot(phi_trans, phi)
I = numpy.identity(phi_trans_phi.shape[0]) # add 1 to nFeature bcos of x0
lamda_I = lamda * I
add_lamda_I_phi_trans_phi = numpy.add(lamda_I, phi_trans_phi)
inv_sum = inv(add_lamda_I_phi_trans_phi) # inverse the matrix above
inv_sum_phi_trans = numpy.dot(inv_sum, phi_trans)
W = numpy.dot(inv_sum_phi_trans, y)
return W
# end def W_lbf
# cross validation for linear basis function
def cv_lbf(data, noutputs, kFolds, lamda, seed):
nFeature = data.shape[1] - noutputs
nSample = data.shape[0]
stra_all = folds_stratify(nSample=nSample, seed=seed, kFolds=kFolds)
df = pandas.DataFrame(index=list(range(0, len(lamda))), columns=list(range(0, noutputs + 1)))
for index, val in enumerate(lamda):
df.iloc[index, 0] = val
error_per_y = pandas.DataFrame(index=list(range(0, kFolds)), columns=list(range(0, noutputs)))
k = 0
while k < kFolds:
stra = stra_all.copy()
test = data[stra[k]]
del stra[k] # del test list
stra_train = list(chain.from_iterable(stra)) # merge the sublists
train = data[stra_train]
w_vals = W_lbf(trainX=train, noutputs=noutputs, lamda=val)
# w_kFolds.append(w_vals)
x_test = test[:, 0:nFeature]
x_test = pandas.DataFrame(x_test)
x_test = pandas.concat([x_test[0], x_test], axis=1) # Adding one column to X
x_test.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
x_test = numpy.asarray(x_test)
y_actual = test[:, nFeature:(nFeature + noutputs)]
y_pred = numpy.dot(x_test, w_vals)
if y_actual.shape != y_pred.shape:
print("\n\nError002: Shape not equal: y_actual.shape != y_pred.shape\n\n")
# y_actual_pred = y_actual - y_pred
y_actual_pred = numpy.subtract(y_actual, y_pred)
error_2 = numpy.square(y_actual_pred)
errors = numpy.sum(error_2, axis=0)
i = 0
# compute error for each y
while i < noutputs:
error_per_y.iloc[k, i] = errors[i]
i += 1
k += 1
j = 0
while j < noutputs:
df.iloc[index, j + 1] = numpy.average(error_per_y.iloc[:, j], axis=0)
j += 1
return df
# end def W_lbf
# cross validation for linear basis function
def cv_lbf_all_data(data, noutputs, kFolds, lamda, seed):
nFeature = data.shape[1] - noutputs
nSample = data.shape[0]
stra_all = folds_stratify(nSample=nSample, seed=seed, kFolds=kFolds)
df = pandas.DataFrame(index=["error"], columns=list(range(0, noutputs)))
index = 0
errors_per_y = []
while index < noutputs:
error_per_fold = []
val = lamda[index]
k = 0
while k < kFolds:
stra = stra_all.copy()
test = data[stra[k]]
del stra[k] # del test list
stra_train = list(chain.from_iterable(stra)) # merge the sublists
train = data[stra_train]
train = pandas.DataFrame(train)
x = train.iloc[:, 0:nFeature]
y = train.iloc[:, nFeature + index] # get the current y
trainX_new = numpy.asarray(x.join(y))
y_actual = test[:, nFeature + index] # get the current y
x_test = test[:, 0:nFeature]
x_test = pandas.DataFrame(x_test)
x_test = pandas.concat([x_test[0], x_test], axis=1) # Adding one column to X
x_test.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
x_test = numpy.asarray(x_test)
w_vals = W_lbf(trainX=trainX_new, noutputs=1, lamda=val)
y_pred = numpy.dot(x_test, w_vals)
y_actual.shape = (y_actual.shape[0], 1)
if y_actual.shape != y_pred.shape:
print("\n\nError002: Shape not equal: y_actual.shape != y_pred.shape\n\n")
# y_actual_pred = y_actual - y_pred
y_actual_pred = numpy.subtract(y_actual, y_pred)
error_2 = numpy.square(y_actual_pred)
errors = numpy.sum(error_2)
error_per_fold.append(errors)
k += 1
index += 1
errors_per_y.append(numpy.mean(error_per_fold))
return [errors_per_y, numpy.sum(errors_per_y)]
# end def cv_lbf
# main lbf function
def lbf_main(trainX, testX, noutputs, nFeature):
kFolds = 5
trainX_pandas = pandas.DataFrame(trainX)
lamda = [0, 0.001, 0.01, 0.1, 10, 100, 1000] # λ
seed = 3221226
df_lbf = cv_lbf(data=trainX, noutputs=noutputs, kFolds=kFolds, lamda=lamda, seed=seed) # do CV to pick the best lamda
print("\nTable of average error(kFold CV) per lamda per the target variable(s)\n" + df_lbf.to_string() + "\n")
lamda = df_lbf.iloc[:, 0]
column = ['best_lamda_per_y', 'error']
df_best_lbf = pandas.DataFrame(index=list(range(0, noutputs)), columns=column)
u = 0
while u < noutputs:
err_al = list(df_lbf.iloc[:, u + 1])
minerr = min(err_al)
ind_best_lam = err_al.index(minerr)
df_best_lbf.iloc[u, 0] = lamda[ind_best_lam]
df_best_lbf.iloc[u, 1] = minerr
u += 1
# end while
print("\nTable of best lamda per the target variable(s) - {the index correspond to the y(s)}\n" + df_best_lbf.to_string() + "\n")
columns_lbf = ['y', 'error_per_y', 'W_per_y']
df_lbf_final = pandas.DataFrame(index=list(range(0, noutputs)), columns=columns_lbf)
# df_lbf_final['y'] = list(range(noutputs))
print("\nHaving chosen the best set(s) of lamda:")
print("Below are the analysis of training the best parameters on trainX and evaluating on testX:\n")
joins = []
u_lbf = 0
while u_lbf < noutputs:
x = trainX_pandas.iloc[:, 0:nFeature]
y = trainX_pandas.iloc[:, nFeature + u_lbf] # get the current y
trainX_new = numpy.asarray(x.join(y))
w_vals_lbf = W_lbf(trainX=trainX_new, noutputs=1, lamda=df_best_lbf["best_lamda_per_y"][u_lbf])
x_test_lbf = testX[:, 0:nFeature]
x_test_lbf = pandas.DataFrame(x_test_lbf)
x_test_lbf = pandas.concat([x_test_lbf[0], x_test_lbf], axis=1) # Adding one column to X
x_test_lbf.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
x_test_lbf = numpy.asarray(x_test_lbf)
y_actual_lbf = testX[:, nFeature + u_lbf]
y_actual_lbf.shape = (y_actual_lbf.shape[0], 1)
y_pred_lbf = numpy.dot(x_test_lbf, w_vals_lbf)
y_actual_pred = numpy.subtract(y_actual_lbf, y_pred_lbf)
error_2_lbf = numpy.square(y_actual_pred)
error = sum(error_2_lbf) # / 2
df_lbf_final['error_per_y'][u_lbf] = error
df_lbf_final['W_per_y'][u_lbf] = w_vals_lbf
df_lbf_final['y'][u_lbf] = u_lbf
join1 = pandas.DataFrame(y_actual_lbf, columns=['y_actual_lbf'])
join2 = pandas.DataFrame(y_pred_lbf, columns=['y_pred_lbf'])
join3 = pandas.DataFrame(y_actual_pred, columns=['y_actual_pred'])
join4 = pandas.DataFrame(error_2_lbf, columns=['error_2_lbf'])
join = pandas.concat([join1, join2, join3, join4], axis=1)
joins.append(join)
print("\n" + "Summary table of test data relating to y{}\n".format(u_lbf) + join.head(5).to_string() + "\n")
# plot
print("y_actual vs. predict for variable y{} \n".format(u_lbf))
matplot.scatter(y_actual_lbf, y_pred_lbf)
matplot.xlabel('y_actual_lbf')
matplot.ylabel('y_pred_lbf')
matplot.show()
u_lbf += 1
# end while
print(df_lbf_final.to_string() + "\n")
print("The total error = {}".format(sum(chain.from_iterable(df_lbf_final["error_per_y"]))))
return [df_lbf_final, joins, df_best_lbf["best_lamda_per_y"]]
# end lbf_main
# phi for Gaussian basis function train and test #same centres have to be used
def phi_gbf(x_train, x_test, nCentre, scale):
centre_loc = list(range(0, nCentre)) # location of the centres
random.shuffle(centre_loc) # shuffle the centres
nRow = x_train.shape[0]
nRow_test = x_test.shape[0]
X_p = pandas.DataFrame(x_train)
X_p_test = pandas.DataFrame(x_test)
phi_train = pandas.DataFrame(index=list(range(0, nRow)), columns=list(range(0, nCentre)))
phi_test = pandas.DataFrame(index=list(range(0, nRow_test)), columns=list(range(0, nCentre)))
centre = None
for index, pos in enumerate(centre_loc):
if pos >= x_train.shape[0]:
randomList = list(range(0, x_train.shape[0]))
random.shuffle(randomList)
pos = randomList[0]
centre = numpy.asarray(X_p.iloc[pos]) + list(chain.from_iterable(0.001*numpy.random.rand(1, X_p.shape[1]))) #ADDING NOISE to avoid same col or row
else:
centre = numpy.asarray(X_p.iloc[pos])
centre = numpy.asarray(centre)
dis = linalg.norm(X_p - centre, axis=1)
phi_cell = numpy.exp(-numpy.square(dis) / (2 * numpy.square(scale)))
phi_train.iloc[:, index] = phi_cell
dis_test = linalg.norm(X_p_test - centre, axis=1)
phi_cell_test = numpy.exp(-numpy.square(dis_test) / (2 * numpy.square(scale)))
phi_test.iloc[:, index] = phi_cell_test
return [phi_train, phi_test]
#end phi_gbf
def W_gbf(phi, y, lamda): # linear basis function# lamda = reqularization coefficient
phi_p = pandas.DataFrame(phi)
X = pandas.concat([phi_p[0], phi_p], axis=1) # Adding one column to X
X.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
X = numpy.asarray(X)
phi = X # in linear basis function #ϕ
phi_trans = phi.transpose() # phi transpose
phi_trans_phi = numpy.dot(phi_trans, phi)
I = numpy.identity(phi_trans_phi.shape[0]) # add 1 to nFeature bcos of x0
lamda_I = lamda * I
add_lamda_I_phi_trans_phi = numpy.add(lamda_I, phi_trans_phi)
inv_sum = inv(add_lamda_I_phi_trans_phi) # inverse the matrix above
inv_sum_phi_trans = numpy.dot(inv_sum, phi_trans)
W = numpy.dot(inv_sum_phi_trans, y)
return W
#end W_gbf
# cross validation for gaussian basis function
def cv_gbf(data, noutputs, kFolds, lamda, width, centre, seed):
nFeature = data.shape[1] - noutputs
nSample = data.shape[0]
stra_all = folds_stratify(nSample=nSample, seed=seed, kFolds=kFolds)
columns = None
if noutputs == 1:
columns = ["Combinations of: lamda, s, u", "error-y0"]
elif noutputs == 3:
columns = ["Combinations of: lamda, s, u", "error-y0" , "error-y1", "error-y2"]
#list(range(0, noutputs + 1))
df = pandas.DataFrame(index=list(range(0, len(lamda) * len(width) * len(centre))), columns=columns)
n = 0
while n < noutputs:
r = 0
for lamda_ind, lamda_val in enumerate(lamda):
for width_ind, width_val in enumerate(width):
for centre_ind, centre_val in enumerate(centre):
df.iloc[r, 0] = [lamda_val, width_val, centre_val]
seed = 1209345 + centre_ind #maintaining the same centre locations for each y(s)
k = 0
err_folds = []
while k < kFolds:
stra = stra_all.copy()
test = data[stra[k]]
del stra[k] # del test list
stra_train = list(chain.from_iterable(stra)) # merge the sublists
train = data[stra_train]
train_x = train[:, 0:nFeature]
train_y = train[:, nFeature + n]
test_x = test[:, 0:nFeature]
y_actual = test[:, nFeature + n]
y_actual.shape = (y_actual.shape[0], 1)
phi_gbf_total = phi_gbf(x_train=train_x, x_test=test_x, nCentre=centre_val, scale=width_val)
phi_train = phi_gbf_total[0]
phi_test = phi_gbf_total[1]
w_vals = W_gbf(phi=phi_train, y=train_y, lamda=lamda_val)
#calculating error
#print("crazy smart {}".format([lamda_val, width_val, centre_val]))
w_vals.shape = (w_vals.shape[0], 1)
phi_test = pandas.DataFrame(phi_test)
phi_test = pandas.concat([phi_test[0], phi_test], axis=1) # Adding one column to X
phi_test.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
phi_test = numpy.asarray(phi_test)
y_pred = numpy.dot(phi_test, w_vals)
if y_actual.shape != y_pred.shape:
print("\n\nError003: Shape not equal: y_actual.shape != y_pred.shape\n\n")
y_actual_pred = numpy.subtract(y_actual, y_pred)
error_2 = numpy.square(y_actual_pred)
errors = numpy.sum(error_2, axis=0)
err_folds.append(errors)
k += 1
aver_err_fold = sum(err_folds) / len(err_folds)
df.iloc[r, n+1] = aver_err_fold
r += 1
n += 1
return df
# end def cv_lbf
def cv_gbf_all_data(data, noutputs, kFolds, lamda, width, centre, seed):
nFeature = data.shape[1] - noutputs
nSample = data.shape[0]
stra_all = folds_stratify(nSample=nSample, seed=seed, kFolds=kFolds)
n = 0
errors_per_y = []
while n < noutputs:
error_per_fold = []
lamda_ = lamda[n]
width_ = width[n]
centre_ = centre[n]
k = 0
while k < kFolds:
stra = stra_all.copy()
test = data[stra[k]]
del stra[k] # del test list
stra_train = list(chain.from_iterable(stra)) # merge the sublists
train = data[stra_train]
y_actual = test[:, nFeature + n]
y_actual.shape = (y_actual.shape[0], 1)
train_x = train[:, 0:nFeature]
train_y = train[:, nFeature + n]
test_x = test[:, 0:nFeature]
y_actual = test[:, nFeature + n]
y_actual.shape = (y_actual.shape[0], 1)
phi_gbf_total = phi_gbf(x_train=train_x, x_test=test_x, nCentre=centre_, scale=width_)
phi_train = phi_gbf_total[0]
phi_test = phi_gbf_total[1]
w_vals = W_gbf(phi=phi_train, y=train_y, lamda=lamda_)
# calculating error
w_vals.shape = (w_vals.shape[0], 1)
phi_test = pandas.DataFrame(phi_test)
phi_test = pandas.concat([phi_test[0], phi_test], axis=1) # Adding one column to X
phi_test.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
phi_test = numpy.asarray(phi_test)
y_pred = numpy.dot(phi_test, w_vals)
if y_actual.shape != y_pred.shape:
print("\n\nError003: Shape not equal: y_actual.shape != y_pred.shape\n\n")
y_actual_pred = numpy.subtract(y_actual, y_pred)
error_2 = numpy.square(y_actual_pred)
errors = numpy.sum(error_2, axis=0)
error_per_fold.append(errors)
k += 1
n += 1
errors_per_y.append(numpy.mean(error_per_fold))
return [errors_per_y, numpy.sum(errors_per_y)]
#validation for gaussian basis function
def gbf_main(train, test, noutputs, lamda, width, centre):
nFeature = train.shape[1] - noutputs
columns = None
if noutputs == 1:
columns = ["error-y0"]
elif noutputs == 3:
columns = ["error-y0" , "error-y1", "error-y2"]
#list(range(0, noutputs + 1))
df = pandas.DataFrame(index=["error"], columns=columns)
actual_pred = []
err = []
n = 0
while n < noutputs:
lamda_ = lamda[n]
width_ = width[n]
centre_ = centre[n]
df_actual_pred = pandas.DataFrame(index=list(range(0, test.shape[0])), columns=["actual", "predict"])
train_x = train[:, 0:nFeature]
train_y = train[:, nFeature + n]
test_x = test[:, 0:nFeature]
y_actual = test[:, nFeature + n]
y_actual.shape = (y_actual.shape[0], 1)
phi_gbf_total = phi_gbf(x_train=train_x, x_test=test_x, nCentre=centre_, scale=width_)
phi_train = phi_gbf_total[0]
phi_test = phi_gbf_total[1]
w_vals = W_gbf(phi=phi_train, y=train_y, lamda=lamda_)
# calculating error
w_vals.shape = (w_vals.shape[0], 1)
phi_test = pandas.DataFrame(phi_test)
phi_test = pandas.concat([phi_test[0], phi_test], axis=1) # Adding one column to X
phi_test.iloc[:, 0] = 1 # setting x0 = 1, from the column added above
phi_test = numpy.asarray(phi_test)
y_pred = numpy.dot(phi_test, w_vals)
if y_actual.shape != y_pred.shape:
print("\n\nError003: Shape not equal: y_actual.shape != y_pred.shape\n\n")
y_actual_pred = numpy.subtract(y_actual, y_pred)
error_2 = numpy.square(y_actual_pred)
errors = numpy.sum(error_2, axis=0)
err.append(errors)
df_actual_pred["actual"] = y_actual
df_actual_pred["predict"] = y_pred
actual_pred.append(df_actual_pred)
print("\n" + "Summary table of test data relating to y{}\n".format(n) + df_actual_pred.head(5).to_string() + "\n")
# plot
print("y_actual vs. predict for variable y{} \n".format(n))
matplot.scatter(y_actual, y_pred)
matplot.xlabel('y_actual_gbf')
matplot.ylabel('y_pred_gbf')
matplot.show()
n += 1
finalError = sum(err)
return finalError
# end def cv_lbf
# my_regression
def my_regression(trainX, testX, noutputs):
columns = None
row = ["best_params", "best_error"]
if noutputs == 1:
columns = ["y0"]
elif noutputs == 3:
columns = ["y0", "y1", "y2"]
nFeature = trainX.shape[1] - noutputs # No of features
#Regularised linear basis function######################################################
df_lbf = lbf_main(trainX, testX, noutputs, nFeature)
#end linear basis function##############################################################
print("\n########## End of Linear Basis Function ############\n")
print("\n########## Beginning of Gaussian Basis Function ############\n")
#Regularised guassian basis function####################################################
#CV on these sets of lamda, width, and centre; to determine the best parameters
seed_gbf = 3745193
kFolds_gbf = 5
lamdas_gbf = [0, 0.001, 1, 10]
widths_gbf = [1, 2, 5, 10, 100]
centres_gbf = [10, 100, 500, 1000]
cv_gbf_best_param = cv_gbf(data=trainX, noutputs=noutputs, kFolds=kFolds_gbf, lamda=lamdas_gbf, width=widths_gbf, centre=centres_gbf, seed=seed_gbf)
print("\n5-Folds Cross Validation\nTable of average error per parameters combinations per the target variable(s)" + "[lamda, width, centres]\n" + cv_gbf_best_param.to_string() + "\n")
params_gbf_cv = cv_gbf_best_param.iloc[:, 0]
df_gbf = pandas.DataFrame(index=row, columns=columns)
total_error = []
n = 0
lamdas_ = []
widths_ = []
centres_ = []
while n < noutputs:
error_gbf_cv = list(chain.from_iterable(cv_gbf_best_param.iloc[:, n + 1]))
minerr = numpy.min(error_gbf_cv)
total_error.append(minerr)
index_best_param = error_gbf_cv.index(minerr)
best_params = params_gbf_cv[index_best_param]
df_gbf.iloc[0, n] = best_params
df_gbf.iloc[1, n] = minerr
lamdas_.append(best_params[0])
widths_.append(best_params[1])
centres_.append(best_params[2])
print("\n" + "The best sets of parameters for y{} are".format(n))
print("lamda = {}".format(best_params[0]))
print("width(s) = {}".format(best_params[1]))
print("No of centers = {}".format(best_params[2]) + "\n")
n += 1
print("\nThe total error accross all target variable(s) = {}".format(sum(total_error)))
print("\nHaving chosen the best set of params:")
print("\nBelow are the analysis of training the best parameters on trainX and evaluating on testX:")
gbf_use_best_param = gbf_main(train=trainX, test=testX, noutputs=noutputs, lamda=lamdas_, width=widths_, centre=centres_)
print("\n########## End of Gaussian Basis Function ############\n")
# end guassian basis function#############################################################
return [df_lbf, df_gbf, [lamdas_, widths_, centres_]]
# end def my_regression
#CV for all dataset
def CV_on_all_data_lbf_gbf(allData, lbf_param, gbf_param, noutputs, dataName):
kFolds = 5
lamda_lbf = lbf_param
seed_lbf = 650932
seed_gbf = 7587930
cv_all_data_lbf = cv_lbf_all_data(data=allData, noutputs=noutputs, kFolds=kFolds, lamda=numpy.asarray(lbf_param[2]), seed=seed_lbf)
print("Linear BF ERROR for " + dataName + " = {}".format(cv_all_data_lbf[1]))
cv_all_data_gbf = cv_gbf_all_data(data=allData, noutputs=noutputs, kFolds=kFolds, lamda=numpy.asarray(gbf_param[0]), width=numpy.asarray(gbf_param[1]), centre=numpy.asarray(gbf_param[2]), seed=seed_gbf)
print("Gaussian BF ERROR for " + dataName + " = {}".format(cv_all_data_gbf[1]))
print("\nFinally, Gaussian BF is the best model for this data")
return [cv_all_data_lbf, cv_all_data_gbf]
####################################### Import Data #########################################
os.chdir('C:/Users/Documents/pycharm/ML/Regression') #set new directory
def z_score_norm(data):
if type(data) is numpy.ndarray:
mean = numpy.mean(data, axis=0)
#data.mean()
std = numpy.std(data, axis=0) #data.std()
data_norm = (data - mean) / std
result = data_norm
else:
result = "Error001: Provide numpy array"
return result
'''AIRFOIL'''
#from numpy import loadtxt
airfoil = numpy.loadtxt("airfoil_self_noise.dat.txt")
sample_size_af = airfoil.shape[0]
airfoil_norm = z_score_norm(airfoil)
random.seed(5054123) #set seed
x_train_af, x_test_af = sklearn.model_selection.train_test_split(airfoil_norm, test_size=0.2, random_state=0)
noutputs_af = 1
'''YACHT'''
yacht = numpy.loadtxt("yacht_hydrodynamics.data.txt")
sample_size_yt = yacht.shape[0]
yacht_norm = z_score_norm(yacht)
random.seed(3452332)
x_train_yt, x_test_yt = sklearn.model_selection.train_test_split(yacht_norm, test_size=0.2, random_state=0)
noutputs_yt = 1
'''SLUMP'''
slump = numpy.loadtxt("slump_test.data.txt", skiprows=1, delimiter=",")
slump = slump[:,1:11]
sample_size_sp = slump.shape[0]
slump_norm = z_score_norm(slump)
random.seed(3450423)
x_train_sp, x_test_sp = sklearn.model_selection.train_test_split(slump_norm, test_size=0.2, random_state=0)
noutputs_sp = 3
####################################### End Import Data #########################################
#################### Airfoil Data - Linear Basis Funcition And Gaussian Basis Function ################################
myReg_airfoil = my_regression(trainX=x_train_af, testX=x_test_af, noutputs=noutputs_af)
print("\nCROSS VALIDATION OUTSIDE my_Regrssion FUNCTION\n5 FOLDS CROSS VALIDATION FOR ALL AIRFOIL DATA: RESULT")
cv_all_data_airfoil = CV_on_all_data_lbf_gbf(allData=airfoil_norm, lbf_param=myReg_airfoil[0], gbf_param=myReg_airfoil[2], noutputs=noutputs_af, dataName="Airfoil")
#myReg__airfoil_jupyter = my_regression(trainX=x_train_af, testX=x_test_af, noutputs=noutputs_af)
#################### End of Airfoil Data ################################
#################### Yacht Data - Linear Basis Funcition And Gaussian Basis Function ################################
myReg_yacht = my_regression(trainX=x_train_yt, testX=x_test_yt, noutputs=noutputs_yt)
print("\nCROSS VALIDATION OUTSIDE my_Regrssion FUNCTION\n5 FOLDS CROSS VALIDATION FOR ALL YACHT DATA: RESULT")
cv_all_data_yacht = CV_on_all_data_lbf_gbf(allData=yacht_norm, lbf_param=myReg_yacht[0], gbf_param=myReg_yacht[2], noutputs=noutputs_yt, dataName="Yacht")
#################### End of Yacht Data ################################
#################### Slump Data - Linear Basis Funcition And Gaussian Basis Function ################################
myReg_slump = my_regression(trainX=x_train_sp, testX=x_test_sp, noutputs=noutputs_sp)
print("\nCROSS VALIDATION OUTSIDE my_Regrssion FUNCTION\n5 FOLDS CROSS VALIDATION FOR ALL SLUMP DATA: RESULT")
cv_all_data_slump = CV_on_all_data_lbf_gbf(allData=slump_norm, lbf_param=myReg_slump[0], gbf_param=myReg_slump[2], noutputs=noutputs_sp, dataName="Slump")
#################### End of Slump Data ################################