# feature_list_of_all_instances.append(l[0:519])
# class_list_of_all_instances.append(int(l[519]))
i += 1
#
# if i == Total_data_number:
# break
c = 0
print("Total instances ", len(total_matrix))
print("Total Features ", len(total_matrix[0]) -1)
print("Starting To Standardize Total Matrix ... ")
# print(total_matrix[0][882:])
total_matrix = standardalize.std(total_matrix, 882, 522 + 73)
# print(total_matrix[0][882:])
print("Total instances ", len(total_matrix))
print("Total Features ", len(total_matrix[0])-1)
for l in total_matrix:
index = len(l) -1
# print(index)
feature_list_of_all_instances.append(l[0:index])
class_list_of_all_instances.append(l[index])
for i in class_list_of_all_instances:
if i == 1:
c += 1
print("Positive data ", c)
# print(l[1286:1295])
total_matrix.append(l)
# b = l[1294]
# if b == 1.0 :#and b != 0:
# i += 1
# print( "count of 2 class " ,i, " value ",b)
# if index % 10000 == 0:
# print(index)
# break
# if i == Total_data_number:
# break
print( "count of 2 class " ,i)
print("length of total matrix ", len(total_matrix) )
print("Starting To Standardize Total Matrix ... ")
total_matrix = standardalize.std(total_matrix, 882, 412)
for l in total_matrix:
feature_list_of_all_instances.append(l[0:1294])
class_list_of_all_instances.append(l[1294])
for i in range(0, Total_data_number):
data.append(i)
kf = cross_validation.KFold(Total_data_number, n_folds=5)
# Cs = numpy.logspace(-6, -1, 10)
# clf = GridSearchCV(estimator='svc',param_grid=dict(C = Cs) , n_jobs=-1 )
l = x.rstrip('\n').split(',')
l = list(map(float, l))
# total_matrix.append(l)
index = len(l) - 1
# print(index)
feature_list_of_all_instances.append(l[0:index])
class_list_of_all_instances.append(l[index])
# feature_list_of_all_instances.append(l[0:519])
# class_list_of_all_instances.append(int(l[519]))
i += 1
#
# if i == Total_data_number:
# break
print("Starting To Standardize Total Matrix ... ")
feature_list_of_all_instances = standardalize.std(
feature_list_of_all_instances, 882, 400)
c = 0
print("Total instances ", len(feature_list_of_all_instances))
print("Total Features ", len(feature_list_of_all_instances[0]))
gc.collect()
# for l in total_matrix:
# index = len(l) -1
# # print(index)
# feature_list_of_all_instances.append(l[0:index])
# class_list_of_all_instances.append(l[index])
# total_matrix = []