Improved code quality based on Codacy report #133

Author: dpuenteramirez

semisupervised/CoTraining.py

@@ -91,7 +91,7 @@ def fit(self, samples, y):
                                             size=2 * self.p + 2 * self.n,
                                             replace=False, shuffle=False)
             except ValueError:
-                print(f'The model was incorrectly parametrized, k is to big.')
+                print('The model was incorrectly parametrized, k is to big.')
             try:
                 u_prime = np.concatenate((u_prime, u[u_random_index]))
             except IndexError:

semisupervised/DemocraticCoLearning.py

@@ -306,17 +306,17 @@ def predict(self, samples):
             breakpoint()
 
         confidence = [0 for _ in range(self.n_labels)]
-        for j in range(len(gj)):
-            izq = (gj[j] + 0.5) / (gj[j] + 1)
-            div = True if gj[j] != 0 else False
+        for index, j in enumerate(gj):
+            izq = (j + 0.5) / (j + 1)
+            div = True if j != 0 else False
             if div:
-                der = [(gj_h[0][j] * self.w1) / gj[j],
-                       (gj_h[1][j] * self.w2) / gj[j],
-                       (gj_h[2][j] * self.w3) / gj[j]]
+                der = [(gj_h[0][index] * self.w1) / gj[index],
+                       (gj_h[1][index] * self.w2) / gj[index],
+                       (gj_h[2][index] * self.w3) / gj[index]]
             else:
                 der = [1 for _ in range(self.n_classifiers)]
 
-            confidence[j] = sum([izq * d for d in der]) / len(der)
+            confidence[index] = sum([izq * d for d in der]) / len(der)
 
         labels = []
         pred1 = self.h1.predict(samples)