imageData = np.array(image)
npd = feature.NPDFeature(imageData)
features.append(npd.extract())
AdaBoostClassifier.save(features, 'features.dump')
features = np.array(features)
print(features.shape)
X_train, X_val, y_train, y_val = train_test_split(features,
y,
test_size=0.25)
classifier = AdaBoostClassifier(DecisionTreeClassifier, 5)
classifier.fit(X_train, y_train)
score = classifier.predict_scores(X_val, y_val)
predict = classifier.predict(X_val)
y_val = np.array(list(map(lambda x: int(x), y_val.reshape(1, -1)[0])))
predict = np.array(list(map(lambda x: int(x), predict.reshape(1, -1)[0])))
print(predict)
print(y_val)
reportContent = 'score = ' + str(score) + '\n'
reportContent += classification_report(y_val, predict)
with open('classifier_report.txt', 'w') as report:
report.write(reportContent)
pass
load_img()
npd_feature()
img_features = np.array(img_features)
img_labels = np.array(img_labels).reshape((-1, 1))
print(img_features.shape)
print(img_features)
X_train, X_val, y_train, y_val = train_test_split(img_features,
img_labels,
test_size=0.25)
print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
ada = AdaBoostClassifier(DecisionTreeClassifier, WEAKERS_LIMIT)
ada.fit(X_train, y_train)
y_predict = ada.predict(X_val)
acc = ada.predict_scores(X_val, y_val)
print(acc)
y_val = np.array(list(map(lambda x: int(x), y_val.reshape(1, -1)[0])))
y_predict = np.array(
list(map(lambda x: int(x),
y_predict.reshape(1, -1)[0])))
print(y_predict)
print(y_val)
reportContent = 'Accuracy = ' + str(acc) + '\n'
reportContent += classification_report(y_val, y_predict)
with open('report.txt', 'w') as report:
