回答編集履歴
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@@ -9,4 +9,64 @@
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```
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キーの意味を知りたければ、以下を参考に。
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http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
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http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
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---
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追記:
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コメントの内容を全部まとめてコードを書き直すと、以下の通り。
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走らせる環境がないので、タイプミス・インデントミスがあるかもしれません。あしからず。
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```python
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# coding:utf-8
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from sklearn import svm, cross_validation
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from sklearn.metrics import classification_report, accuracy_score
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import sys
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from mfcc import *
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import glob
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import csv
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import random
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import numpy as np
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import os
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from sklearn.model_selection import train_test_split
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def get_data(files, nfft, nceps):
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data = None
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label = np.array([])
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for file_name in files:
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feature = get_feature(file_name, nfft, nceps)
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if data is None:
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data = feature
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else:
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data = np.vstack((data, feature))
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if file_name.split('/')[-1].startswith('dog'):
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label = np.append(label, 0)
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else:
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label = np.append(label, 1)
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return data, label
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if __name__ == "__main__":
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nfft = 2048 # FFTのサンプル数
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nceps = 12 # MFCCの次元数
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basedir = '/sound_animal/sounds'
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files = glob.glob(os.path.join(basedir, '*.wav'))
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data, label = get_data(files, nfft, nceps)
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train_data, test_data, train_label, test_label = train_test_split(data, label, test_size=0.33, random_state=0, stratify=label)
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feature_train_data = np.hstack((train_label.reshape(-1, 1), train_data))
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feature_test_data = np.hstack((test_label.reshape(-1, 1), test_data))
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with open("feature_data/train_data.txt", "w") as f:
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writer = csv.writer(f)
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writer.writerows(feature_train_data)
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with open("feature_data/test_data.txt", "w") as f:
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writer = csv.writer(f)
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writer.writerows(feature_test_data)
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clf = svm.SVC(kernel='linear', C=1)
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clf.fit(train_data, train_label)
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score = clf.score(test_data, test_label)
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print(score)
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```
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