Wrap lines that go beyond GitHub Editor (#1925)

* Wrap lines that go beyond GiHub Editor

* flake8 --count --select=E501 --max-line-length=127

* updating DIRECTORY.md

* Update strassen_matrix_multiplication.py

* fixup! Format Python code with psf/black push

* Update decision_tree.py

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

machine_learning/decision_tree.py

@@ -20,15 +20,21 @@ class Decision_Tree:
         mean_squared_error:
         @param labels: a one dimensional numpy array
         @param prediction: a floating point value
-        return value: mean_squared_error calculates the error if prediction is used to estimate the labels
+        return value: mean_squared_error calculates the error if prediction is used to
+            estimate the labels
         >>> tester = Decision_Tree()
         >>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
         >>> test_prediction = np.float(6)
-        >>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction)
+        >>> tester.mean_squared_error(test_labels, test_prediction) == (
+        ...     Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
+        ...         test_prediction))
+        True
         >>> test_labels = np.array([1,2,3])
         >>> test_prediction = np.float(2)
-        >>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction)
-
+        >>> tester.mean_squared_error(test_labels, test_prediction) == (
+        ...     Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
+        ...         test_prediction))
+        True
         """
         if labels.ndim != 1:
             print("Error: Input labels must be one dimensional")
@@ -46,7 +52,8 @@ class Decision_Tree:
         """
 
         """
-        this section is to check that the inputs conform to our dimensionality constraints
+        this section is to check that the inputs conform to our dimensionality
+        constraints
         """
         if X.ndim != 1:
             print("Error: Input data set must be one dimensional")
@@ -72,7 +79,8 @@ class Decision_Tree:
         """
         loop over all possible splits for the decision tree. find the best split.
         if no split exists that is less than 2 * error for the entire array
-        then the data set is not split and the average for the entire array is used as the predictor
+        then the data set is not split and the average for the entire array is used as
+        the predictor
         """
         for i in range(len(X)):
             if len(X[:i]) < self.min_leaf_size:
@@ -136,7 +144,7 @@ class Test_Decision_Tree:
         helper_mean_squared_error_test:
         @param labels: a one dimensional numpy array
         @param prediction: a floating point value
-        return value: helper_mean_squared_error_test calculates the mean squared error 
+        return value: helper_mean_squared_error_test calculates the mean squared error
         """
         squared_error_sum = np.float(0)
         for label in labels:
@@ -147,9 +155,10 @@ class Test_Decision_Tree:
 
 def main():
     """
-    In this demonstration we're generating a sample data set from the sin function in numpy.
-    We then train a decision tree on the data set and use the decision tree to predict the
-    label of 10 different test values. Then the mean squared error over this test is displayed.
+    In this demonstration we're generating a sample data set from the sin function in
+    numpy.  We then train a decision tree on the data set and use the decision tree to
+    predict the label of 10 different test values. Then the mean squared error over
+    this test is displayed.
     """
     X = np.arange(-1.0, 1.0, 0.005)
     y = np.sin(X)

machine_learning/logistic_regression.py

@@ -1,6 +1,6 @@
 #!/usr/bin/python
 
-## Logistic Regression from scratch
+# Logistic Regression from scratch
 
 # In[62]:
 
@@ -8,8 +8,12 @@
 
 # importing all the required libraries
 
-""" Implementing logistic regression for classification problem
-     Helpful resources : 1.Coursera ML course    2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac"""
+"""
+Implementing logistic regression for classification problem
+Helpful resources:
+Coursera ML course
+https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
+"""
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -21,7 +25,8 @@ from sklearn import datasets
 
 # In[67]:
 
-# sigmoid function or logistic function is used as a hypothesis function in classification problems
+# sigmoid function or logistic function is used as a hypothesis function in
+# classification problems
 
 
 def sigmoid_function(z):

machine_learning/support_vector_machines.py

@@ -3,6 +3,7 @@ from sklearn import svm
 from sklearn.model_selection import train_test_split
 import doctest
 
+
 # different functions implementing different types of SVM's
 def NuSVC(train_x, train_y):
     svc_NuSVC = svm.NuSVC()
@@ -17,8 +18,11 @@ def Linearsvc(train_x, train_y):
 
 
 def SVC(train_x, train_y):
-    # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
-    # various parameters like "kernel","gamma","C" can effectively tuned for a given machine learning model.
+    # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,
+    # probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,
+    # max_iter=-1, random_state=None)
+    # various parameters like "kernel","gamma","C" can effectively tuned for a given
+    # machine learning model.
     SVC = svm.SVC(gamma="auto")
     SVC.fit(train_x, train_y)
     return SVC
@@ -27,8 +31,8 @@ def SVC(train_x, train_y):
 def test(X_new):
     """
     3 test cases to be passed
-    an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm)
-    based on which  the target name will be predicted
+    an array containing the sepal length (cm), sepal width (cm), petal length (cm),
+    petal width (cm) based on which  the target name will be predicted
     >>> test([1,2,1,4])
     'virginica'
     >>> test([5, 2, 4, 1])