def score(self, x_test, y_test):
"""确定当前模型的准确度
:param x_test:
:param y_test:
:return:
"""
y_predict = self.predict(x_test)
return accuracy_score(y_test, y_predict)
def score(self, x_test, y_test):
"""算法准确率"""
y_predict = self.predict(x_test)
y_predict = np.array(y_predict)
return accuracy_score(y_test, y_predict)
"""
import sys
from time import time
sys.path.append("../tools/")
from email_preprocess import preprocess
### features_train and features_test are the features for the training
### and testing datasets, respectively
### labels_train and labels_test are the corresponding item labels
features_train, features_test, labels_train, labels_test = preprocess()
#########################################################
### your code goes here ###
from sklearn.naive_bayes import GaussianNB
from metric import accuracy_score
clf = GaussianNB()
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
score = accuracy_score(labels_test,pred)
print score
#########################################################
def acc(self, y, p):
return accuracy_score(np.argmax(y, axis=1), np.argmax(p, axis=1))
def score(self, X, y):
predictions = self.predict(X)
return accuracy_score(y, predictions)
def score(self, x_test, y_test):
y_predict = self.predict(x_test)
return accuracy_score(y_test, y_predict)
# 提取每一个类别下的特征值的方差 以及 均值
sample_feature = sample[j]
# 计算高斯密度
likelihood = self._calculate_likelihood(
params["mean"], params["var"], sample_feature)
posterior *= likelihood
posteriors.append(posterior)
# 求最大概率对应的类别
index_of_max = np.argmax(posteriors)
return self.classes[index_of_max]
def predict(self, X):
y_pred = []
for sample in X:
y = self._classify(sample)
y_pred.append(y)
return y_pred
if __name__ == '__main__':
from sklearn import datasets
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = NaiveBayes()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(accuracy_score(y_pred, y_test))
if __name__ == '__main__':
X, y = gen_mult_ser(3000)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = NeuralNetwork(optimizer=optimizer, loss=CrossEntropy)
clf.add(RNN(10, activation="tanh", bptt_trunc=5, input_shape=(10, 61)))
clf.add(Activation('softmax'))
tmp_X = np.argmax(X_train[0], axis=1)
tmp_y = np.argmax(y_train[0], axis=1)
print("Number Series Problem:")
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y = [" + " ".join(tmp_y.astype("str")) + "]")
print()
train_err, _ = clf.fit(X_train, y_train, n_epochs=500, batch_size=512)
y_pred = np.argmax(clf.predict(X_test), axis=2)
y_test = np.argmax(y_test, axis=2)
accuracy = np.mean(accuracy_score(y_test, y_pred))
print(accuracy)
print()
print("Results:")
for i in range(5):
tmp_X = np.argmax(X_test[i], axis=1)
tmp_y1 = y_test[i]
tmp_y2 = y_pred[i]
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y_true = [" + " ".join(tmp_y1.astype("str")) + "]")
print("y_pred = [" + " ".join(tmp_y2.astype("str")) + "]")
print()
def score(self, x_test, y_test):
"""当前模型的分类准确度"""
y_predict = self.predict(x_test)
return accuracy_score(y_test, y_predict)