本文整理汇总了Python中hpelm.HPELM类的典型用法代码示例。如果您正苦于以下问题：Python HPELM类的具体用法？Python HPELM怎么用？Python HPELM使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了HPELM类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_AddNeurons_InitDefault_BiasWNotZero
def test_AddNeurons_InitDefault_BiasWNotZero(self):
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "sigm")
W = hpelm.nnet.get_neurons()[0][2]
bias = hpelm.nnet.get_neurons()[0][3]
self.assertGreater(np.sum(np.abs(W)), 0.001)
self.assertGreater(np.sum(np.abs(bias)), 0.001)示例2: test_MultilabelError_CorrectWithMultipleClasses
def test_MultilabelError_CorrectWithMultipleClasses(self):
T = np.zeros((100, 5))
T[:, 0] = 1
Y = np.zeros((100, 5))
Y[:, 1] = 1
model = HPELM(1, 5, classification='ml')
self.assertEqual(0.4, model.error(T, Y))示例3: test_SLFN_AddTwoNeuronTypes_GotThem
def test_SLFN_AddTwoNeuronTypes_GotThem(self):
hpelm = HPELM(1, 1)
hpelm.add_neurons(1, "lin")
hpelm.add_neurons(1, "sigm")
self.assertEquals(2, len(hpelm.nnet.get_neurons()))
ntypes = [nr[1] for nr in hpelm.nnet.get_neurons()]
self.assertIn("lin", ntypes)
self.assertIn("sigm", ntypes)示例4: test_MultiLabelClassError_Works
def test_MultiLabelClassError_Works(self):
T = self.makeh5(np.array([[0, 1], [1, 1], [1, 0]]))
Y = self.makeh5(np.array([[0.4, 0.6], [0.8, 0.6], [1, 1]]))
hpelm = HPELM(1, 2)
hpelm.add_neurons(1, "lin")
hpelm.classification = "ml"
e = hpelm.error(T, Y)
np.testing.assert_allclose(e, 1.0 / 6)示例5: test_AddDataAsyncToFile_SingleAddition
def test_AddDataAsyncToFile_SingleAddition(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data_async(X, T, fHH=fHH, fHT=fHT)示例6: test_RegressionError_Works
def test_RegressionError_Works(self):
T = np.array([1, 2, 3])
Y = np.array([1.1, 2.2, 3.3])
err1 = np.mean((T - Y) ** 2)
fT = self.makeh5(T)
fY = self.makeh5(Y)
hpelm = HPELM(1, 1)
e = hpelm.error(fT, fY)
np.testing.assert_allclose(e, err1)示例7: test_AddNeurons_InitTwiceBiasW_CorrectlyMerged
def test_AddNeurons_InitTwiceBiasW_CorrectlyMerged(self):
hpelm = HPELM(2, 1)
W1 = np.random.rand(2, 3)
W2 = np.random.rand(2, 4)
bias1 = np.random.rand(3,)
bias2 = np.random.rand(4,)
hpelm.add_neurons(3, "sigm", W1, bias1)
hpelm.add_neurons(4, "sigm", W2, bias2)
np.testing.assert_array_almost_equal(np.hstack((W1, W2)), hpelm.nnet.get_neurons()[0][2])
np.testing.assert_array_almost_equal(np.hstack((bias1, bias2)), hpelm.nnet.get_neurons()[0][3])示例8: test_PredictAsync_Works
def test_PredictAsync_Works(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(1, "lin")
hpelm.train(X, T)
fY = self.makefile()
hpelm.predict_async(X, fY)示例9: test_Project_Works
def test_Project_Works(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(1, "lin")
hpelm.train(X, T)
fH = self.makefile()
hpelm.project(X, fH)示例10: test_AddDataToFile_MixedSequentialAsync
def test_AddDataToFile_MixedSequentialAsync(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
hpelm.add_data_async(X, T, fHH=fHH, fHT=fHT)示例11: test_TrainIcount_HasEffect
def test_TrainIcount_HasEffect(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6]]))
T = self.makeh5(np.array([[3], [2], [3]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(1, "lin")
hpelm.train(X, T)
B1 = hpelm.nnet.get_B()
hpelm.train(X, T, icount=2)
B2 = hpelm.nnet.get_B()
self.assertFalse(np.allclose(B1, B2), "iCount index does not work")示例12: test_SolveCorr_Works
def test_SolveCorr_Works(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
hpelm.solve_corr(fHH, fHT)
self.assertIsNot(hpelm.nnet.get_B(), None)示例13: test_ValidationCorr_ReturnsConfusion
def test_ValidationCorr_ReturnsConfusion(self):
X = self.makeh5(np.random.rand(10, 3))
T = self.makeh5(np.random.rand(10, 2))
hpelm = HPELM(3, 2, classification="c")
hpelm.add_neurons(6, "tanh")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
_, _, confs = hpelm.validation_corr(fHH, fHT, X, T, steps=3)
self.assertGreater(np.sum(confs[0]), 1)示例14: test_ValidationCorr_Works
def test_ValidationCorr_Works(self):
X = self.makeh5(np.random.rand(30, 3))
T = self.makeh5(np.random.rand(30, 2))
hpelm = HPELM(3, 2, norm=1e-6)
hpelm.add_neurons(6, "tanh")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
nns, err, confs = hpelm.validation_corr(fHH, fHT, X, T, steps=3)
self.assertGreater(err[0], err[-1])示例15: test_WeightedClassError_Works
def test_WeightedClassError_Works(self):
X = self.makeh5(np.array([1, 2, 3]))
T = self.makeh5(np.array([[0, 1], [0, 1], [1, 0]]))
Y = self.makeh5(np.array([[0, 1], [0.4, 0.6], [0, 1]]))
# here class 0 is totally incorrect, and class 1 is totally correct
w = (9, 1)
hpelm = HPELM(1, 2)
hpelm.add_neurons(1, "lin")
hpelm.train(X, T, "wc", w=w)
e = hpelm.error(T, Y)
np.testing.assert_allclose(e, 0.9)