from sklearn.model_selection import train_test_split

Author: GuangxiaoSong

0100_rawdata_svm.py

@@ -9,7 +9,7 @@
 from __future__ import print_function
 import data.load_raw_data_file_to_array as f2a
 import numpy as np
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
 
 import time

0200_svm.py

@@ -8,7 +8,7 @@
 
 from __future__ import print_function
 import numpy as np
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
 
 data = np.loadtxt('data/transAllData.txt')
@@ -32,4 +32,6 @@
     gamma=0.00024, kernel='rbf', max_iter=-1, probability=False,
     random_state=None, shrinking=True, tol=0.001, verbose=False)
 clf.fit(data_train, label_train)
+print (clf.predict(data_test))
+print (label_test)
 print (clf.score(data_test, label_test))

0300_multi-layer_perceptron.py

@@ -9,7 +9,7 @@
 from __future__ import print_function
 import numpy as np
 from sklearn.neural_network import MLPClassifier
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 data = np.loadtxt('data/transAllData.txt')
 num = 100 # 每种风格音乐文件个数

0400_nearest_centroid.py

@@ -7,7 +7,7 @@
 """
 
 import numpy as np
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.neighbors.nearest_centroid import NearestCentroid
 
 data = np.loadtxt('data/transAllData.txt')

0504_tf_full_connect_NN.py

@@ -136,7 +136,7 @@ def multilayer_perceptron(x, weights, biases):
     # Start input enqueue threads.
     coord = tf.train.Coordinator()
     threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-    for epoch in range(10000):
+    for epoch in range(100):
         # pass it in through the feed_dict
         audio_batch_vals, label_batch_vals = sess.run([audio_batch, label_batch])
 
@@ -187,8 +187,8 @@ def multilayer_perceptron(x, weights, biases):
         9
         '''
 
-        if (label_test_val == pred_class_index[0]):
+        if label_test_val == pred_class_index[0]:
             correct_num += 1
-    print (("%i / %i is correct.") % (correct_num, test_example_number))
-    print (("Accuracy is %f .") % (float(correct_num) / test_example_number))
+    print("%i / %i is correct." % (correct_num, test_example_number))
+    print("Accuracy is %f ." % (float(correct_num) / test_example_number))
     sess.close()