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Generate_Data.py
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Generate_Data.py
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import numpy as np
import pandas as pd
import os
import errno
def my_relu(x):
return x*(x>0)
a = 1
b = 1
#-----------------Regression Data------------------------_#
for my_seed in range(1,11):
np.random.seed(my_seed)
TotalP = 2000
print('p = ', TotalP)
NTrain = 10000
x_train = np.matrix(np.zeros([NTrain, TotalP]))
y_train = np.matrix(np.zeros([NTrain, 1]))
sigma = 1.0
for i in range(NTrain):
if i%1000 == 0:
print("x_train generate = ", i)
ee = np.sqrt(sigma) * np.random.normal(0, 1)
for j in range(TotalP):
x_train[i, j] = (a*ee + b*np.sqrt(sigma) * np.random.normal(0, 1)) / np.sqrt(a*a+b*b)
x0 = x_train[i, 0]
x1 = x_train[i, 1]
x2 = x_train[i, 2]
x3 = x_train[i, 3]
x4 = x_train[i, 4]
y_train[i, 0] = 5 * x1 / (1 + x0 * x0) + 5 * np.sin(x2 * x3) + 2 * x4 + np.random.normal(0, 1)
Nval = 1000
x_val = np.matrix(np.zeros([Nval, TotalP]))
y_val = np.matrix(np.zeros([Nval, 1]))
sigma = 1.0
for i in range(Nval):
ee = np.sqrt(sigma) * np.random.normal(0, 1)
for j in range(TotalP):
x_val[i, j] = (a*ee + b*np.sqrt(sigma) * np.random.normal(0, 1)) / np.sqrt(a*a+b*b)
x0 = x_val[i, 0]
x1 = x_val[i, 1]
x2 = x_val[i, 2]
x3 = x_val[i, 3]
x4 = x_val[i, 4]
y_val[i, 0] = 5 * x1 / (1 + x0 * x0) + 5 * np.sin(x2 * x3) + 2 * x4 + np.random.normal(0, 1)
NTest = 1000
x_test = np.matrix(np.zeros([NTest, TotalP]))
y_test = np.matrix(np.zeros([NTest, 1]))
for i in range(NTest):
ee = np.sqrt(sigma) * np.random.normal(0, 1)
for j in range(TotalP):
x_test[i, j] = (a*ee + b*np.sqrt(sigma) * np.random.normal(0, 1)) / np.sqrt(a*a+b*b)
x0 = x_test[i, 0]
x1 = x_test[i, 1]
x2 = x_test[i, 2]
x3 = x_test[i, 3]
x4 = x_test[i, 4]
y_test[i, 0] = 5 * x1 / (1 + x0 * x0) + 5 * np.sin(x2 * x3) + 2 * x4 + np.random.normal(0, 1)
x_train_df = pd.DataFrame(x_train)
y_train_df = pd.DataFrame(y_train)
x_val_df = pd.DataFrame(x_val)
y_val_df = pd.DataFrame(y_val)
x_test_df = pd.DataFrame(x_test)
y_test_df = pd.DataFrame(y_test)
PATH = './data/regression/' + str(my_seed) + "/"
if not os.path.isdir(PATH):
try:
os.makedirs(PATH)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(PATH):
pass
else:
raise
print("write train")
x_train_df.to_csv(PATH + "x_train.csv")
y_train_df.to_csv(PATH + "y_train.csv")
print("write val")
x_val_df.to_csv(PATH + "x_val.csv")
y_val_df.to_csv(PATH + "y_val.csv")
print('write test')
x_test_df.to_csv(PATH + "x_test.csv")
y_test_df.to_csv(PATH + "y_test.csv")
#--------------------Structure Selection Data-----------------------------#
for my_seed in range(1,11):
np.random.seed(my_seed)
TotalP = 1000
print('p = ', TotalP)
NTrain = 10000
x_train = np.matrix(np.zeros([NTrain, TotalP]))
y_train = np.matrix(np.zeros([NTrain, 1]))
sigma = 0.5
for i in range(NTrain):
if i%1000 == 0:
print("x_train generate = ", i)
ee = np.sqrt(sigma) * np.random.normal(0, 1)
for j in range(TotalP):
x_train[i, j] = (a*ee + b*np.sqrt(sigma) * np.random.normal(0, 1)) / np.sqrt(a*a+b*b)
x0 = x_train[i, 0]
x1 = x_train[i, 1]
x2 = x_train[i, 2]
x3 = x_train[i, 3]
x4 = x_train[i, 4]
y_train[i, 0] = np.tanh(2 * np.tanh(2 * x0 - x1)) + 2*np.tanh(
2* np.tanh(x2 - 2 * x3 - 2*x4 )) + np.random.normal(0, 1)
Nval = 1000
x_val = np.matrix(np.zeros([Nval, TotalP]))
y_val = np.matrix(np.zeros([Nval, 1]))
sigma = 1.0
for i in range(Nval):
ee = np.sqrt(sigma) * np.random.normal(0, 1)
for j in range(TotalP):
x_val[i, j] = (a*ee + b*np.sqrt(sigma) * np.random.normal(0, 1)) / np.sqrt(a*a+b*b)
x0 = x_val[i, 0]
x1 = x_val[i, 1]
x2 = x_val[i, 2]
x3 = x_val[i, 3]
x4 = x_val[i, 4]
y_val[i, 0] = np.tanh(2 * np.tanh(2 * x0 - x1)) + 2*np.tanh(
2* np.tanh(x2 - 2 * x3 - 2*x4 )) + np.random.normal(0, 1)
NTest = 1000
x_test = np.matrix(np.zeros([NTest, TotalP]))
y_test = np.matrix(np.zeros([NTest, 1]))
for i in range(NTest):
ee = np.sqrt(sigma) * np.random.normal(0, 1)
for j in range(TotalP):
x_test[i, j] = (a*ee + b*np.sqrt(sigma) * np.random.normal(0, 1)) / np.sqrt(a*a+b*b)
x0 = x_test[i, 0]
x1 = x_test[i, 1]
x2 = x_test[i, 2]
x3 = x_test[i, 3]
x4 = x_test[i, 4]
y_test[i, 0] = np.tanh(2 * np.tanh(2 * x0 - x1)) + 2*np.tanh(
2* np.tanh(x2 - 2 * x3 - 2*x4 )) + np.random.normal(0, 1)
x_train_df = pd.DataFrame(x_train)
y_train_df = pd.DataFrame(y_train)
x_val_df = pd.DataFrame(x_val)
y_val_df = pd.DataFrame(y_val)
x_test_df = pd.DataFrame(x_test)
y_test_df = pd.DataFrame(y_test)
PATH = './data/structure/' + str(my_seed) + "/"
if not os.path.isdir(PATH):
try:
os.makedirs(PATH)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(PATH):
pass
else:
raise
print("write train")
x_train_df.to_csv(PATH + "/x_train.csv")
y_train_df.to_csv(PATH + "/y_train.csv")
print("write val")
x_val_df.to_csv(PATH + "/x_val.csv")
y_val_df.to_csv(PATH + "/y_val.csv")
print('write test')
x_test_df.to_csv(PATH + "/x_test.csv")
y_test_df.to_csv(PATH + "/y_test.csv")