worked on venv

ml_algorithms/src/algorithms/cnn/cnn.py

@@ -0,0 +1,7 @@
+def convolution2d(x, kernel, bias, stride=1, padding=0):
+    """
+    Convolution 2D
+    """
+
+
+

ml_algorithms/src/algorithms/linear_regression/batch_gradient_descent/uni_batch_gd_v.py

@@ -13,18 +13,18 @@ def gradient_descent(
 
     a0 = 130
     a1 = 19
-    
+
     a0_prev = a0
     a1_prev = a1
 
     data_set = pd.read_csv(filename, delimiter=',', index_col=False)
 
     m = len(data_set)
     epoch = 0
- 
+
     previous_cost = sys.float_info.max
     gd_data = []
-    
+
 
     while True:
         # calculate the hypothesis function for all training data
@@ -75,7 +75,7 @@ def gradient_descent(
         a0_range=(125,175,0.5),
         a1_range=(18,22,0.5),
         gd_points = gd_data
-        )         
+        )
     return a0, a1
 
 if __name__ == '__main__':
@@ -87,6 +87,7 @@ def gradient_descent(
     epochs_threshold = 100000
     costdifference_threshold = 0.00001
     plot = True
-    
+
     a0, a1 = gradient_descent(filename, alpha, epochs_threshold, costdifference_threshold, plot)
-    print(f'a0: {a0:.3f}, a1: {a1:.3f}')
+    print(f'a0: {a0:.3f}, a1: {a1:.3f}')
+

ml_algorithms/src/algorithms/linear_regression/normalization/normalization_analysis.py

@@ -1,6 +1,6 @@
 import os
 import pandas as pd
-from sklearn.preprocessing import StandardScaler
+
 
 
 data_path=os.path.join(os.path.dirname(__file__),'..', 'data_generation', 'data_1f.csv')

ml_algorithms/src/algorithms/linear_regression/univariate_gd_analysis.py

@@ -1,12 +1,17 @@
 from operator import index
-from typing_extensions import _AnnotatedAlias
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 from matplotlib import cm
 
-def plot_univariate_gd_analysis(file:str, a0_range:tuple, a1_range:tuple, gd_points:list, plot_slices=False):
+def plot_univariate_gd_analysis(
+    file:str,
+    a0_range:tuple,
+    a1_range:tuple,
+    gd_points:list,
+    plot_slices=False):
     '''
+    plot the costs surface and the gradient descent points
     '''
 
     # read the data set
@@ -31,7 +36,6 @@ def plot_univariate_gd_analysis(file:str, a0_range:tuple, a1_range:tuple, gd_poi
             cost_row.append(cost)
         costs.append(cost_row)
 
-
     if plot_slices:
 
         a0_mincost_idx = np.where(np.round(a0[0,:], 1)==150)
@@ -43,7 +47,7 @@ def plot_univariate_gd_analysis(file:str, a0_range:tuple, a1_range:tuple, gd_poi
         plt.plot(a1_mincost, costs_mincosts)
         plt.xlabel(r'$a_1$')
         plt.ylabel(r'$J(150,a_1$)')
-        
+
         plt.show()
 
 
@@ -71,7 +75,15 @@ def plot_univariate_gd_analysis(file:str, a0_range:tuple, a1_range:tuple, gd_poi
     plt.rcParams['text.usetex'] = True
     fig = plt.figure()
     ax = plt.axes(projection='3d')
-    ax.plot_surface(a0, a1, np.array(costs), rstride=1, cstride=1, cmap='cividis', edgecolor='none', alpha=0.5)
+    ax.plot_surface(
+        a0,
+        a1,
+        np.array(costs),
+        rstride=1,
+        cstride=1,
+        cmap='cividis',
+        edgecolor='none',
+        alpha=0.5)
     ax.contour(a0, a1, np.array(costs), zdir='z', offset=-0.5, cmap=cm.coolwarm)
     ax.plot(xx, yy, zz, 'r.--', alpha=1)
     ax.set_xlabel(r'$a_0$')
@@ -84,7 +96,7 @@ def plot_univariate_gd_analysis(file:str, a0_range:tuple, a1_range:tuple, gd_poi
 
     plot_univariate_gd_analysis(
         file=os.path.join(os.path.dirname(__file__), 'data_generation', 'data_1f.csv'),
-        a0_range=(125,175,0.2), 
-        a1_range=(18,22,0.2), 
+        a0_range=(125,175,0.2),
+        a1_range=(18,22,0.2),
         gd_points= [],
         plot_slices=True)

ml_algorithms/src/algorithms/linear_regression/univariate_linear_regression/univariate_lr.py

@@ -26,4 +26,5 @@
 
 a_1 = (n*sum_xy - sum_x*sum_y)/(n*sum_x_sq - sum_x**2)
 
+
 print(f'a_0: {a_0}, a_1: {a_1}')

ml_algorithms/src/algorithms/logistic_regression/binaryclassification.py

@@ -1,8 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
-import h5py
-import scipy
-from scipy import ndimage
+
 
 def sigmoid(z):
     """