Reformat all files that are possible to reformat

code/artificial_intelligence/src/DBSCAN_Clustering/dbscan.py

@@ -6,20 +6,24 @@
 UNCLASSIFIED = False
 NOISE = None
 
-def _dist(p,q):
-	return math.sqrt(np.power(p-q,2).sum())
 
-def _eps_neighborhood(p,q,eps):
-	return _dist(p,q) < eps
+def _dist(p, q):
+    return math.sqrt(np.power(p - q, 2).sum())
+
+
+def _eps_neighborhood(p, q, eps):
+    return _dist(p, q) < eps
+
 
 def _region_query(m, point_id, eps):
     n_points = m.shape[1]
     seeds = []
     for i in range(0, n_points):
-        if _eps_neighborhood(m[:,point_id], m[:,i], eps):
+        if _eps_neighborhood(m[:, point_id], m[:, i], eps):
             seeds.append(i)
     return seeds
 
+
 def _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):
     seeds = _region_query(m, point_id, eps)
     if len(seeds) < min_points:
@@ -36,14 +40,17 @@ def _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):
             if len(results) >= min_points:
                 for i in range(0, len(results)):
                     result_point = results[i]
-                    if classifications[result_point] == UNCLASSIFIED or \
-                       classifications[result_point] == NOISE:
+                    if (
+                        classifications[result_point] == UNCLASSIFIED
+                        or classifications[result_point] == NOISE
+                    ):
                         if classifications[result_point] == UNCLASSIFIED:
                             seeds.append(result_point)
                         classifications[result_point] = cluster_id
             seeds = seeds[1:]
         return True
 
+
 def dbscan(m, eps, min_points):
     """Implementation of Density Based Spatial Clustering of Applications with Noise
     See https://en.wikipedia.org/wiki/DBSCAN
@@ -65,22 +72,29 @@ def dbscan(m, eps, min_points):
     n_points = m.shape[1]
     classifications = [UNCLASSIFIED] * n_points
     for point_id in range(0, n_points):
-        point = m[:,point_id]
+        point = m[:, point_id]
         if classifications[point_id] == UNCLASSIFIED:
-            if _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):
+            if _expand_cluster(
+                m, classifications, point_id, cluster_id, eps, min_points
+            ):
                 cluster_id = cluster_id + 1
     return classifications
 
+
 # def test_dbscan():
 #     m = np.matrix('1 1.2 0.8 3.7 3.9 3.6 10; 1.1 0.8 1 4 3.9 4.1 10')
 #     eps = 0.5
 #     min_points = 2
 #     assert dbscan(m, eps, min_points) == [1, 1, 1, 2, 2, 2, None]
 
+
 def main():
-	m = np.matrix('-0.99 -0.98 -0.97 -0.96 -0.95 0.95 0.96 0.97 0.98 0.99; 1.1 1.09 1.08 1.07 1.06 1.06 1.07 1.08 1.09 1.1')
-	eps = 1
-	min_points = 3
-	print(dbscan(m, eps, min_points))
+    m = np.matrix(
+        "-0.99 -0.98 -0.97 -0.96 -0.95 0.95 0.96 0.97 0.98 0.99; 1.1 1.09 1.08 1.07 1.06 1.06 1.07 1.08 1.09 1.1"
+    )
+    eps = 1
+    min_points = 3
+    print(dbscan(m, eps, min_points))
+
 
 main()

code/artificial_intelligence/src/ISODATA_Clustering/ISODATA.py

@@ -11,7 +11,9 @@ def volume_estimation(cluster, center):
     num_of_points = len(cluster)
     distance = []
     for i in range(num_of_points):
-        distance.append(distance_2point(center[0], center[1], cluster[i][0], cluster[i][1]))
+        distance.append(
+            distance_2point(center[0], center[1], cluster[i][0], cluster[i][1])
+        )
 
     return sum(distance) / num_of_points
 
@@ -33,7 +35,9 @@ def center_distance(centers):
             if i == j:
                 pass
             else:
-                D_ij[(i, j)] = distance_2point(centers[i][0], centers[i][1], centers[j][0], centers[j][1])
+                D_ij[(i, j)] = distance_2point(
+                    centers[i][0], centers[i][1], centers[j][0], centers[j][1]
+                )
         k += 1
     return D_ij
 
@@ -66,7 +70,11 @@ def cluster_points_distribution(centers, points):
     for i in range(points_len):
         # iteration throught all centers
         for j in range(centers_len):
-            distance.append(distance_2point(centers[j][0], centers[j][1], points[i][0], points[i][1]))
+            distance.append(
+                distance_2point(
+                    centers[j][0], centers[j][1], points[i][0], points[i][1]
+                )
+            )
         distances.append(distance)
         distance = []
 
@@ -210,8 +218,12 @@ def clusterize():
                 length = len(clusters[i])
                 coef = 2 * (THETA_N + 1)
 
-                if (max_s[i] > THETA_S) and ((D_vol[i] > D and length > coef) or N_c < float(K) / 2):
-                    center1, center2 = cluster_division(clusters[i], centers[i], vectors[i])
+                if (max_s[i] > THETA_S) and (
+                    (D_vol[i] > D and length > coef) or N_c < float(K) / 2
+                ):
+                    center1, center2 = cluster_division(
+                        clusters[i], centers[i], vectors[i]
+                    )
                     del centers[i]
                     centers.append(center1)
                     centers.append(center2)
@@ -221,14 +233,14 @@ def clusterize():
                     pass
 
         # for i in clusters:
-        #	print(i)
+        # 	print(i)
 
         # step 11
         D_ij = center_distance(centers)
         rang = {}
         for coord in D_ij:
             if D_ij[coord] < THETA_C:
-                rang[coord] = (D_ij[coord])
+                rang[coord] = D_ij[coord]
             else:
                 pass
 
@@ -245,7 +257,7 @@ def clusterize():
     return clusters
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # if file called as a script
     # cluster points
     points1 = [
@@ -261,7 +273,7 @@ def clusterize():
         (5, -6),
         (6, -5),
         (44, 49),
-        (45, 50)
+        (45, 50),
     ]
     points2 = [
         (-0.99, 1.09),
@@ -276,7 +288,7 @@ def clusterize():
         (0.96, 1.06),
         (0.97, 1.07),
         (0.98, 1.08),
-        (0.99, 1.09)
+        (0.99, 1.09),
     ]
     points3 = [
         (-99, 109),
@@ -291,13 +303,9 @@ def clusterize():
         (96, 106),
         (97, 107),
         (98, 108),
-        (99, 109)
+        (99, 109),
     ]
     points = points3
     cl = clusterize()
     for i in cl:
         print("Cl", i)
-
-
-
-

code/artificial_intelligence/src/Linear_Regression/linear_regression.py

@@ -33,8 +33,8 @@ def plot_regression_line(x, y, b):
     plt.plot(x, y_pred, color="r")
 
     # putting labels
-    plt.xlabel('x')
-    plt.ylabel('y')
+    plt.xlabel("x")
+    plt.ylabel("y")
 
     # function to show plot
     plt.show()
@@ -47,8 +47,12 @@ def main():
 
     # estimating coefficients
     b = estimate_coef(x, y)
-    print("Estimated coefficients are:\nb_0 = {}  \
-          \nb_1 = {}".format(b[0], b[1]))
+    print(
+        "Estimated coefficients are:\nb_0 = {}  \
+          \nb_1 = {}".format(
+            b[0], b[1]
+        )
+    )
 
     # plotting regression line
     plot_regression_line(x, y, b)

code/artificial_intelligence/src/Logistic_Regression/Logistic_Regression.py

@@ -14,11 +14,9 @@ def log_likelihood(features, target, weights):
     return ll
 
 
-def logistic_regression(features,
-                        target,
-                        num_steps,
-                        learning_rate,
-                        add_intercept=False):
+def logistic_regression(
+    features, target, num_steps, learning_rate, add_intercept=False
+):
     if add_intercept:
         intercept = np.ones((features.shape[0], 1))
         features = np.hstack((intercept, features))
@@ -44,21 +42,19 @@ def logistic_regression(features,
 np.random.seed(12)
 num_observations = 5000
 
-x1 = np.random.multivariate_normal([0, 0], [[1, .75], [.75, 1]],
-                                   num_observations)
-x2 = np.random.multivariate_normal([1, 4], [[1, .75], [.75, 1]],
-                                   num_observations)
+x1 = np.random.multivariate_normal([0, 0], [[1, 0.75], [0.75, 1]], num_observations)
+x2 = np.random.multivariate_normal([1, 4], [[1, 0.75], [0.75, 1]], num_observations)
 
 simulated_separableish_features = np.vstack((x1, x2)).astype(np.float32)
-simulated_labels = np.hstack((np.zeros(num_observations),
-                              np.ones(num_observations)))
+simulated_labels = np.hstack((np.zeros(num_observations), np.ones(num_observations)))
 
 plt.figure(figsize=(12, 8))
 plt.scatter(
     simulated_separableish_features[:, 0],
     simulated_separableish_features[:, 1],
     c=simulated_labels,
-    alpha=.4)
+    alpha=0.4,
+)
 
 plt.show()
 
@@ -69,6 +65,7 @@ def logistic_regression(features,
     simulated_labels,
     num_steps=300000,
     learning_rate=5e-5,
-    add_intercept=True)
+    add_intercept=True,
+)
 
 print(weights)

code/artificial_intelligence/src/artificial_neural_network/ann.py

@@ -2,11 +2,12 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-dataset = pd.read_csv('dataset.csv')
+dataset = pd.read_csv("dataset.csv")
 X = dataset.iloc[:, 3:13].values
 y = dataset.iloc[:, 13].values
 
 from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+
 labelencoder_X_1 = LabelEncoder()
 X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
 labelencoder_X_2 = LabelEncoder()
@@ -16,10 +17,11 @@
 X = X[:, 1:]
 
 from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.2, random_state=0)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
 
 from sklearn.preprocessing import StandardScaler
+
 sc = StandardScaler()
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test)
@@ -31,26 +33,16 @@
 classifier = Sequential()
 
 classifier.add(
-    Dense(
-        units=6,
-        kernel_initializer='uniform',
-        activation='relu',
-        input_dim=11))
+    Dense(units=6, kernel_initializer="uniform", activation="relu", input_dim=11)
+)
 
-classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))
+classifier.add(Dense(units=6, kernel_initializer="uniform", activation="relu"))
 
-classifier.add(
-    Dense(
-        units=1,
-        kernel_initializer='uniform',
-        activation='sigmoid'))
+classifier.add(Dense(units=1, kernel_initializer="uniform", activation="sigmoid"))
 
-classifier.compile(
-    optimizer='adam',
-    loss='binary_crossentropy',
-    metrics=['accuracy'])
+classifier.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
 
 classifier.fit(X_train, y_train, batch_size=10, epochs=100)
 
 y_pred = classifier.predict(X_test)
-y_pred = (y_pred > 0.5)
+y_pred = y_pred > 0.5