pyupgrade --py37-plus **/*.py (#1654)

* pyupgrade --py37-plus **/*.py

* fixup! Format Python code with psf/black push

machine_learning/k_means_clust.py

@@ -126,7 +126,7 @@ def plot_heterogeneity(heterogeneity, k):
     plt.plot(heterogeneity, linewidth=4)
     plt.xlabel("# Iterations")
     plt.ylabel("Heterogeneity")
-    plt.title("Heterogeneity of clustering over time, K={0:d}".format(k))
+    plt.title(f"Heterogeneity of clustering over time, K={k:d}")
     plt.rcParams.update({"font.size": 16})
     plt.show()
 
@@ -164,7 +164,7 @@ def kmeans(
             num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
             if verbose:
                 print(
-                    "    {0:5d} elements changed their cluster assignment.".format(
+                    "    {:5d} elements changed their cluster assignment.".format(
                         num_changed
                     )
                 )

machine_learning/logistic_regression.py

@@ -1,5 +1,4 @@
 #!/usr/bin/python
-# -*- coding: utf-8 -*-
 
 ## Logistic Regression from scratch
 

machine_learning/sequential_minimum_optimization.py

@@ -1,4 +1,3 @@
-# coding: utf-8
 """
     Implementation of sequential minimal optimization(SMO) for support vector machines(SVM).
 
@@ -29,7 +28,6 @@ Reference:
     http://web.cs.iastate.edu/~honavar/smo-svm.pdf
 """
 
-from __future__ import division
 
 import os
 import sys
@@ -44,7 +42,7 @@ from sklearn.preprocessing import StandardScaler
 CANCER_DATASET_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"
 
 
-class SmoSVM(object):
+class SmoSVM:
     def __init__(
         self,
         train,
@@ -405,7 +403,7 @@ class SmoSVM(object):
         return self.samples.shape[0]
 
 
-class Kernel(object):
+class Kernel:
     def __init__(self, kernel, degree=1.0, coef0=0.0, gamma=1.0):
         self.degree = np.float64(degree)
         self.coef0 = np.float64(coef0)