Repair error about the definition of the axis for the multiplication

Mathias
1 parent 4aa3a0ea73
Showing 1 changed file with 15 additions and 3 deletions Side-by-side Diff
volia/measures.py
@@ -169,10 +169,15 @@
         return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
  
     def compute_purity_score(count_matrix, axis=0):
+        if axis==0:
+            other_axis = 1
+        else:
+            other_axis = 0
         count_per_row = count_matrix.sum(axis=axis)
         dividers = np.square(count_per_row)
+
         count_matrix_squared = np.square(count_matrix)
-        matrix_divided = np.apply_along_axis(divide_line, 0, np.asarray(count_matrix_squared, dtype=np.float), dividers)
+        matrix_divided = np.apply_along_axis(divide_line, other_axis, np.asarray(count_matrix_squared, dtype=np.float), dividers)
         vector_purity = np.sum(matrix_divided, axis=axis)
  
         scalar_purity = np.average(vector_purity, weights=count_per_row)
@@ -186,7 +191,6 @@
     K = np.sqrt(purity_cluster_score * purity_class_score)
  
     for i in range(count_matrix.shape[0]):
-
         for j in range(count_matrix.shape[1]):
             count_matrix[i][j]
         count_matrix[i]
  
  
  
@@ -198,15 +202,23 @@
  
  
 if __name__ == "__main__":
+    print("Purity test #1")
     # Hypothesis
     y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0])
     # Truth
     y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
  
     (result_matrix, result_vector, result) = entropy_score(y, y_hat)
+    print(purity_score(y, y_hat))
  
+    exit(1)
+    print("Purity test #2")
+    # Hypothesis
+    y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0, 4, 4, 4])
+    # Truth
+    y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 0, 3, 3, 3])
  
-    print(purity_score(y, y_hat))
+    (result_matrix, result_vector, result) = entropy_score(y, y_hat)
     exit(1)
     print("Result matrix: ")
     print(result_matrix)
...	...	@@ -169,10 +169,15 @@
169	169	return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
170	170
171	171	def compute_purity_score(count_matrix, axis=0):
	172	+ if axis==0:
	173	+ other_axis = 1
	174	+ else:
	175	+ other_axis = 0
172	176	count_per_row = count_matrix.sum(axis=axis)
173	177	dividers = np.square(count_per_row)
	178	+
174	179	count_matrix_squared = np.square(count_matrix)
175		- matrix_divided = np.apply_along_axis(divide_line, 0, np.asarray(count_matrix_squared, dtype=np.float), dividers)
	180	+ matrix_divided = np.apply_along_axis(divide_line, other_axis, np.asarray(count_matrix_squared, dtype=np.float), dividers)
176	181	vector_purity = np.sum(matrix_divided, axis=axis)
177	182
178	183	scalar_purity = np.average(vector_purity, weights=count_per_row)
...	...	@@ -186,7 +191,6 @@
186	191	K = np.sqrt(purity_cluster_score * purity_class_score)
187	192
188	193	for i in range(count_matrix.shape[0]):
189		-
190	194	for j in range(count_matrix.shape[1]):
191	195	count_matrix[i][j]
192	196	count_matrix[i]
193	197
194	198
195	199
...	...	@@ -198,15 +202,23 @@
198	202
199	203
200	204	if __name__ == "__main__":
	205	+ print("Purity test #1")
201	206	# Hypothesis
202	207	y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0])
203	208	# Truth
204	209	y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
205	210
206	211	(result_matrix, result_vector, result) = entropy_score(y, y_hat)
	212	+ print(purity_score(y, y_hat))
207	213
	214	+ exit(1)
	215	+ print("Purity test #2")
	216	+ # Hypothesis
	217	+ y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0, 4, 4, 4])
	218	+ # Truth
	219	+ y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 0, 3, 3, 3])
208	220
209		- print(purity_score(y, y_hat))
	221	+ (result_matrix, result_vector, result) = entropy_score(y, y_hat)
210	222	exit(1)
211	223	print("Result matrix: ")
212	224	print(result_matrix)