Python TrendLearner示例

示例#1

文件： classify_pts_test.py 项目： chrinide/pyksc

def fit(Xtrain, y_train, Xtest, num_pts):

    learner = TrendLearner(num_pts, 1)
    learner.fit(Xtrain, y_train)
    probs  = learner.predict_proba(Xtest)
        
    return probs

示例#2

def fit(Xtrain, y_train, Xtest, num_pts):

    learner = TrendLearner(num_pts, 1)
    learner.fit(Xtrain, y_train)
    probs  = learner.predict_proba(Xtest)
        
    return probs

示例#3

文件： test_trend.py 项目： flaviovdf/pyksc

    def test_predict_good(self):

        base_one = np.ones(10)
        base_two = np.array([90, 2000, 90, 2000, 90, 2000, 90, 2000, 90, 2000])

        y = []
        X = []
        for _ in range(10):
            X.append(self.addnoise(base_one))
            X.append(self.addnoise(base_two))
            y.append(1)
            y.append(0)

        l = TrendLearner(3, 1)
        l.fit(X, y)

        P = []
        for _ in range(50):
            P.append(self.addnoise(base_one))
            P.append(self.addnoise(base_two))

        predict = l.predict(P)
        self.assertEqual(50, sum(predict == 0))
        self.assertEqual(50, sum(predict == 1))

        probs = l.predict_proba(P)

        for i in xrange(probs.shape[0]):
            if i % 2 == 0:
                self.assertTrue(probs[i, 1] > probs[i, 0])
            else:
                self.assertTrue(probs[i, 0] > probs[i, 1])

示例#4

文件： classify_pts_all.py 项目： flaviovdf/pyksc

def fit(Xtrain, y_train, Xtest, num_pts):

    learner = TrendLearner(num_pts, 1)
    learner.fit(Xtrain, y_train)

    probs = learner.predict_proba(Xtest)
    y_pred = probs.argmax(axis=1)

    return y_pred, probs

示例#5

文件： classify_pts.py 项目： evenchange4/pyksc

def fit(C, y_train, X, y_true, num_pts):

    learner = TrendLearner(num_pts, 1)
    learner.fit(C, y_train)

    probs = learner.predict_proba(X)
    y_pred = probs.argmax(axis=1)

    return y_pred, probs

示例#6

    def test_predict_good(self):

        base_one = np.ones(10)
        base_two = np.array([90, 2000, 90, 2000, 90, 2000, 90, 2000, 90, 2000])

        y = []
        X = []
        for _ in range(10):
            X.append(self.addnoise(base_one))
            X.append(self.addnoise(base_two))
            y.append(1)
            y.append(0)

        l = TrendLearner(3, 1)
        l.fit(X, y)

        P = []
        for _ in range(50):
            P.append(self.addnoise(base_one))
            P.append(self.addnoise(base_two))

        predict = l.predict(P)
        self.assertEqual(50, sum(predict == 0))
        self.assertEqual(50, sum(predict == 1))

        probs = l.predict_proba(P)

        for i in xrange(probs.shape[0]):
            if i % 2 == 0:
                self.assertTrue(probs[i, 1] > probs[i, 0])
            else:
                self.assertTrue(probs[i, 0] > probs[i, 1])

示例#7

文件： test_trend.py 项目： flaviovdf/pyksc

    def test_predict_bad(self):

        base_one = np.ones(10)
        base_two = np.array([90, 2000, 90, 2000, 90, 2000, 90, 2000, 90, 2000])

        y = []
        X = []
        for _ in range(10):
            X.append(self.addnoise(base_one))
            X.append(self.addnoise(base_two))
            y.append(1)
            y.append(0)

        l = TrendLearner(1, 1)
        l.fit(X, y)

        P = []
        for _ in range(50):
            P.append(self.addnoise(base_one))
            P.append(self.addnoise(base_two))

        predict = l.predict(P)
        self.assertEqual(100, sum(predict == 0))

示例#8

    def test_predict_bad(self):

        base_one = np.ones(10)
        base_two = np.array([90, 2000, 90, 2000, 90, 2000, 90, 2000, 90, 2000])

        y = []
        X = []
        for _ in range(10):
            X.append(self.addnoise(base_one))
            X.append(self.addnoise(base_two))
            y.append(1)
            y.append(0)

        l = TrendLearner(1, 1)
        l.fit(X, y)

        P = []
        for _ in range(50):
            P.append(self.addnoise(base_one))
            P.append(self.addnoise(base_two))

        predict = l.predict(P)
        self.assertEqual(100, sum(predict == 0))