Insert scatter matrices rank and confidence of guess(improve)

1 files changed, 27 insertions, 17 deletions

diff --git a/train.py b/train.py
index 064d92e..a16cb04 100755
--- a/train.py
+++ b/train.py
@@ -57,7 +57,7 @@ def test_split(n_faces, raw_faces, split, seed):
     faces_test = faces_test.reshape(n_faces*n_test_faces, n_pixels)
     return faces_train, faces_test, target_train, target_test
 
-def draw_conf_mat(target_test, target_pred):
+def draw_conf_mat(args, target_test, target_pred):
     cm = confusion_matrix(target_test, target_pred)
     print(cm)
     if (args.conf_mat):
@@ -66,7 +66,7 @@ def draw_conf_mat(target_test, target_pred):
         plt.ylabel('Actual')
         plt.xlabel('Predicted')
         plt.show()
-    print('Accuracy %fl' % accuracy_score(target_test, target_pred))
+    return accuracy_score(target_test, target_pred)
 
 def test_model(M, faces_train, faces_test, target_train, target_test, args):
     raw_faces_train = faces_train
@@ -108,12 +108,18 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
             ar.imshow(raw_faces_train[args.reconstruct].reshape([46,56]).T, cmap = 'gist_gray')
             plt.show()
 
-    if args.lda or (args.pca and args.lda):
-        lda = LinearDiscriminantAnalysis(n_components=M, solver='eigen')
+    if args.lda:
+        if args.pca_r or (args.pca and M > n_training_faces - n_faces):
+            lda = LinearDiscriminantAnalysis(n_components=M, solver='eigen')
+        else:
+            lda = LinearDiscriminantAnalysis(n_components=M, store_covariance='True')
+
         faces_train = lda.fit_transform(faces_train, target_train)
         faces_test = lda.transform(faces_test)
         class_means = lda.means_
         e_vals = lda.explained_variance_ratio_
+        scatter_matrix = lda.covariance_
+        print(LA.matrix_rank(scatter_matrix))
 
     if args.faces:
         if args.lda:
@@ -133,7 +139,7 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
         plt.show()
 
     if args.grapheigen:
-	graph_eigen()
+        graph_eigen()
         # Colors for distinct individuals
         cols = ['#{:06x}'.format(randint(0, 0xffffff)) for i in range(n_faces)]
         pltCol = [cols[int(k)] for k in target_train]
@@ -144,14 +150,19 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args):
 
     classifier = KNeighborsClassifier(n_neighbors=args.neighbors)
     if (args.reconstruct):
-        classifier.fit(raw_faces_train, target_train)
+        cgassifier.fit(raw_faces_train, target_train)
         target_pred = classifier.predict(rec_faces_test)
         #Better Passing n_neighbors = 1
     else:
         classifier.fit(faces_train, target_train)
         target_pred = classifier.predict(faces_test)
+        targer_prob = np.max(classifier.predict_proba(faces_test), axis=1).reshape([52,2])
+        targer_prob = np.mean(targer_prob, axis=1)
+        plt.bar(range(52), targer_prob)
+        plt.show()
+
         #Better n_neighbors = 2
-    draw_conf_mat(target_test, target_pred)
+    return draw_conf_mat(args, target_test, target_pred)
 
 def main():
     parser = argparse.ArgumentParser()
@@ -183,18 +194,17 @@ def main():
 
 
     if args.reigen:
-	for M in range(args.eigen, args,reigen):
-		start = time()
-		test_model(M, faces_train, faces_test, target_train, target_test, args)
-		end = time()
-		print("Run with", M, "eigenvalues completed in %.2f" % end-start, "seconds")
+        for M in range(args.eigen, args,reigen):
+            start = timer()
+            accuracy[M] = test_model(M, faces_train, faces_test, target_train, target_test, args)
+            end = timer()
+            print("Run with", M, "eigenvalues completed in ", end-start, "seconds")
+        
     else:
         M = args.eigen
-	start = time()
-	test_model(M, faces_train, faces_test, target_train, target_test, args):
-	end = time()
-	print("Run with", M, "eigenvalues completed in %.2f" % end-start, "seconds")
-
+        start = timer()
+        test_model(M, faces_train, faces_test, target_train, target_test, args)
+        end = timer()
     
 if __name__ == "__main__":
     main()