Set the Python file maximum line length to 88 characters (#2122)

* flake8 --max-line-length=88

* fixup! Format Python code with psf/black push

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

machine_learning/gradient_descent.py

@@ -1,5 +1,6 @@
 """
-Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis function.
+Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis
+function.
 """
 import numpy
 
@@ -75,7 +76,8 @@ def summation_of_cost_derivative(index, end=m):
     :param index: index wrt derivative is being calculated
     :param end: value where summation ends, default is m, number of examples
     :return: Returns the summation of cost derivative
-    Note: If index is -1, this means we are calculating summation wrt to biased parameter.
+    Note: If index is -1, this means we are calculating summation wrt to biased
+        parameter.
     """
     summation_value = 0
     for i in range(end):
@@ -90,7 +92,8 @@ def get_cost_derivative(index):
     """
     :param index: index of the parameter vector wrt to derivative is to be calculated
     :return: derivative wrt to that index
-    Note: If index is -1, this means we are calculating summation wrt to biased parameter.
+    Note: If index is -1, this means we are calculating summation wrt to biased
+        parameter.
     """
     cost_derivative_value = summation_of_cost_derivative(index, m) / m
     return cost_derivative_value

machine_learning/polymonial_regression.py

@@ -10,7 +10,8 @@ from sklearn.preprocessing import PolynomialFeatures
 
 # Importing the dataset
 dataset = pd.read_csv(
-    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/position_salaries.csv"
+    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
+    "position_salaries.csv"
 )
 X = dataset.iloc[:, 1:2].values
 y = dataset.iloc[:, 2].values

machine_learning/sequential_minimum_optimization.py

@@ -42,7 +42,10 @@ import pandas as pd
 from sklearn.datasets import make_blobs, make_circles
 from sklearn.preprocessing import StandardScaler
 
-CANCER_DATASET_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"
+CANCER_DATASET_URL = (
+    "http://archive.ics.uci.edu/ml/machine-learning-databases/"
+    "breast-cancer-wisconsin/wdbc.data"
+)
 
 
 class SmoSVM:
@@ -124,7 +127,8 @@ class SmoSVM:
             b_old = self._b
             self._b = b
 
-            # 4:  update error value,here we only calculate those non-bound samples' error
+            # 4:  update error value,here we only calculate those non-bound samples'
+            #     error
             self._unbound = [i for i in self._all_samples if self._is_unbound(i)]
             for s in self.unbound:
                 if s == i1 or s == i2:
@@ -231,8 +235,10 @@ class SmoSVM:
         """
         Choose first alpha ;steps:
            1:First loop over all sample
-           2:Second loop over all non-bound samples till all non-bound samples does not voilate kkt condition.
-           3:Repeat this two process endlessly,till all samples does not voilate kkt condition samples after first loop.
+           2:Second loop over all non-bound samples till all non-bound samples does not
+               voilate kkt condition.
+           3:Repeat this two process endlessly,till all samples does not voilate kkt
+               condition samples after first loop.
         """
         while True:
             all_not_obey = True
@@ -352,8 +358,8 @@ class SmoSVM:
             )
             """
             # way 2
-            Use objective function check which alpha2 new could get the minimal objectives
-
+            Use objective function check which alpha2 new could get the minimal
+            objectives
             """
             if ol < (oh - self._eps):
                 a2_new = L
@@ -572,11 +578,11 @@ def plot_partition_boundary(
     model, train_data, ax, resolution=100, colors=("b", "k", "r")
 ):
     """
-    We can not get the optimum w of our kernel svm model which is different from linear svm.
-    For this reason, we generate randomly distributed points with high desity and prediced values of these points are
-    calculated by using our tained model. Then we could use this prediced values to draw contour map.
+    We can not get the optimum w of our kernel svm model which is different from linear
+    svm.  For this reason, we generate randomly distributed points with high desity and
+    prediced values of these points are calculated by using our tained model. Then we
+    could use this prediced values to draw contour map.
     And this contour map can represent svm's partition boundary.
-
     """
     train_data_x = train_data[:, 1]
     train_data_y = train_data[:, 2]