Travis CI: Add pytest --doctest-modules neural_network

Fixes #987
```
neural_network/perceptron.py:123: in <module>
    sample.insert(i, float(input('value: ')))
../lib/python3.7/site-packages/_pytest/capture.py:693: in read
    raise IOError("reading from stdin while output is captured")
E   OSError: reading from stdin while output is captured
-------------------------------------------------------------------------------- Captured stdout --------------------------------------------------------------------------------
('\nEpoch:\n', 399)
------------------------

value:
```

Author: cclauss

.travis.yml

@@ -14,10 +14,6 @@ script:
       --ignore=data_structures/stacks/infix_to_postfix_conversion.py
       --ignore=file_transfer_protocol/ftp_send_receive.py
       --ignore=file_transfer_protocol/ftp_client_server.py
-      --ignore=machine_learning/linear_regression.py
-      --ignore=machine_learning/perceptron.py
-      --ignore=machine_learning/random_forest_classification/random_forest_classification.py
-      --ignore=machine_learning/random_forest_regression/random_forest_regression.py
       --ignore=maths/abs_min.py
       --ignore=maths/binary_exponentiation.py
       --ignore=maths/lucas_series.py

machine_learning/perceptron.py

@@ -1,17 +1,19 @@
 # Random Forest Classification
 
 # Importing the libraries
+import os
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
 # Importing the dataset
-dataset = pd.read_csv('Social_Network_Ads.csv')
+script_dir = os.path.dirname(os.path.realpath(__file__))
+dataset = pd.read_csv(os.path.join(script_dir, 'Social_Network_Ads.csv'))
 X = dataset.iloc[:, [2, 3]].values
 y = dataset.iloc[:, 4].values
 
 # Splitting the dataset into the Training set and Test set
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 
 # Feature Scaling
@@ -66,4 +68,4 @@
 plt.xlabel('Age')
 plt.ylabel('Estimated Salary')
 plt.legend()
-plt.show()
+plt.show()

machine_learning/random_forest_classification/random_forest_classification.py

@@ -1,12 +1,14 @@
 # Random Forest Regression
 
 # Importing the libraries
+import os
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
 # Importing the dataset
-dataset = pd.read_csv('Position_Salaries.csv')
+script_dir = os.path.dirname(os.path.realpath(__file__))
+dataset = pd.read_csv(os.path.join(script_dir, 'Position_Salaries.csv'))
 X = dataset.iloc[:, 1:2].values
 y = dataset.iloc[:, 2].values
 
@@ -28,7 +30,7 @@
 regressor.fit(X, y)
 
 # Predicting a new result
-y_pred = regressor.predict(6.5)
+y_pred = regressor.predict(6.5).reshape(1, -1)
 
 # Visualising the Random Forest Regression results (higher resolution)
 X_grid = np.arange(min(X), max(X), 0.01)
@@ -38,4 +40,4 @@
 plt.title('Truth or Bluff (Random Forest Regression)')
 plt.xlabel('Position level')
 plt.ylabel('Salary')
-plt.show()
+plt.show()

machine_learning/random_forest_regression/random_forest_regression.py

@@ -113,13 +113,12 @@ def sign(self, u):
 
 exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
 
-if __name__ == '__main__':
-    network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
+network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
 
-    network.training()
+network.training()
 
-    while True:
-        sample = []
-        for i in range(3):
-            sample.insert(i, float(input('value: ').strip()))
-        network.sort(sample)
+while True:
+    sample = []
+    for i in range(3):
+        sample.insert(i, float(input('value: ')))
+    network.sort(sample)

neural_network/perceptron.py

@@ -6,6 +6,7 @@ numpy
 opencv-python
 pandas
 pytest
+requests
 sklearn
 sympy
 tensorflow