Exemple #1
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    def testLinearRegression(self):
        model = ml.linear_regression_gd(self.mtcars, 0.1, 0.1, 10)
        self.assertIsInstance(model, ml.LinearRegressionModel)
        self.assertIsInstance(model.weights, pd.DataFrame)
        self.assertEqual(len(model.weights), 1)
        self.assertEqual(len(model.weights.columns), self.mtcars.ncol)

        self.assertIsInstance(model.predict(range(0, self.mtcars.ncol - 1)), float)
        with self.assertRaises(Py4JJavaError):
            model.predict([0, 1, 2])
Exemple #2
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    def testLinearRegression(self):
        model = ml.linear_regression_gd(self.mtcars, 0.1, 0.1, 10)
        self.assertIsInstance(model, ml.LinearRegressionModel)
        self.assertIsInstance(model.weights, pd.DataFrame)
        self.assertEqual(len(model.weights), 1)
        self.assertEqual(len(model.weights.columns), self.mtcars.ncol)

        self.assertIsInstance(model.predict(range(0, self.mtcars.ncol - 1)),
                              float)
        with self.assertRaises(Py4JJavaError):
            model.predict([0, 1, 2])
Exemple #3
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print('Columns: ' + ', '.join(ddf.colnames))

print('Number of columns: {}'.format(ddf.cols))
print('Number of rows: {}'.format(ddf.rows))

print(ddf.summary())

print(ddf.head(2))

print(ddf.aggregate(['sum(mpg)', 'min(hp)'], ['vs', 'am']))

print(ddf.five_nums())

print(ddf.sample(3))

# Kmeans
km = ml.kmeans(ddf)
clu = km.predict(range(0, ddf.ncol))
print clu

# Linear Regression
lr = ml.linear_regression_gd(ddf)
lr.summary()

# Logistic Regression
lr = ml.logistic_regression_gd(ddf)
lr.summary()

dm.shutdown()