Revision 033cf05be04ac45dc05303d13479e77af7b9ebc7 authored by Cha Zhang on 04 June 2017, 20:42:56 UTC, committed by Cha Zhang on 04 June 2017, 21:17:22 UTC
1 parent 4e7e971
create_data.py
# -*- coding: utf-8 -*-
"""
Copyright (c) Microsoft. All rights reserved.
Licensed under the MIT license. See LICENSE file in the project root for full license information.
"""
import numpy as np
from sklearn.utils import shuffle
# number of dimensions
Dim = 2
# number of samples
N_train = 1000
N_test = 500
def generate(N, mean, cov, diff):
#import ipdb;ipdb.set_trace()
num_classes = len(diff)
samples_per_class = int(N/2)
X0 = np.random.multivariate_normal(mean, cov, samples_per_class)
Y0 = np.zeros(samples_per_class)
for ci, d in enumerate(diff):
X1 = np.random.multivariate_normal(mean+d, cov, samples_per_class)
Y1 = (ci+1)*np.ones(samples_per_class)
X0 = np.concatenate((X0,X1))
Y0 = np.concatenate((Y0,Y1))
X, Y = shuffle(X0, Y0)
return X,Y
def create_data_files(num_classes, diff, train_filename, test_filename, regression):
print("Outputting %s and %s"%(train_filename, test_filename))
mean = np.random.randn(num_classes)
cov = np.eye(num_classes)
for filename, N in [(train_filename, N_train), (test_filename, N_test)]:
X, Y = generate(N, mean, cov, diff)
# output in CNTK Text format
with open(filename, "w") as dataset:
num_labels = int((1 + np.amax(Y)))
for i in range(N):
dataset.write("|features ")
for d in range(Dim):
dataset.write("%f " % X[i,d])
if (regression):
dataset.write("|labels %f\n" % Y[i])
else:
labels = ['0'] * num_labels;
labels[int(Y[i])] = '1'
dataset.write("|labels %s\n" % " ".join(labels))
def main():
# random seed (create the same data)
np.random.seed(10)
create_data_files(Dim, [3.0], "Train_cntk_text.txt", "Test_cntk_text.txt", True)
create_data_files(Dim, [[3.0], [3.0, 0.0]], "Train-3Classes_cntk_text.txt", "Test-3Classes_cntk_text.txt", False)
if __name__ == '__main__':
main()
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