In [1]:

    

# 
# LogisticRegression Example
#
# @author becxer
# @email becxer87@gmail.com
#

import numpy as np
from pytrain.LogisticRegression import LogisticRegression


    

In [2]:

    

# Simple dataset
train_mat = [[0.12, 0.25],\
             [3.24, 4.33],\
             [0.14, 0.45],\
             [7.30, 4.23],]

train_label = [[0,1],\
               [1,0],\
               [0,1],\
               [1,0]]

test_a = [0.10,0.33]
test_b = [4.0,4.5]


    

In [3]:

    

# Train model
logit_reg = LogisticRegression(train_mat, train_label)
logit_reg.fit(lr = 0.001, epoch = 10000, batch_size =4)


    

In [5]:

    

# Test model
res_a = np.rint(logit_reg.predict(test_a))
res_b = np.rint(logit_reg.predict(test_b))

print("X %s => Y %s" % (test_a, res_a))
print("X %s => Y %s" % (test_b, res_b))


    

    




X [0.1, 0.33] => Y [ 0.  1.]
X [4.0, 4.5] => Y [ 1.  0.]