Python numpy.polynomial.legendre.legvander() Examples
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Example #1
Source File: test_legendre.py From recruit with Apache License 2.0 | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #2
Source File: test_legendre.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #3
Source File: test_legendre.py From ImageFusion with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #4
Source File: test_legendre.py From mxnet-lambda with Apache License 2.0 | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #5
Source File: test_legendre.py From coffeegrindsize with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #6
Source File: test_legendre.py From pySINDy with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #7
Source File: test_legendre.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #8
Source File: test_legendre.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #9
Source File: test_legendre.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #10
Source File: test_legendre.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #11
Source File: test_legendre.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #12
Source File: test_legendre.py From Computable with MIT License | 6 votes |
def test_legvander(self) : # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4) : coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4) : coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #13
Source File: test_legendre.py From vnpy_crypto with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #14
Source File: test_legendre.py From keras-lambda with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #15
Source File: test_legendre.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_legvander(self): # check for 1d x x = np.arange(3) v = leg.legvander(x, 3) assert_(v.shape == (3, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef)) # check for 2d x x = np.array([[1, 2], [3, 4], [5, 6]]) v = leg.legvander(x, 3) assert_(v.shape == (3, 2, 4)) for i in range(4): coef = [0]*i + [1] assert_almost_equal(v[..., i], leg.legval(x, coef))
Example #16
Source File: test_legendre.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #17
Source File: test_legendre.py From coffeegrindsize with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #18
Source File: test_legendre.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #19
Source File: test_legendre.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #20
Source File: test_legendre.py From keras-lambda with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #21
Source File: learning_legendre.py From learning-circuits with Apache License 2.0 | 5 votes |
def _setup(self, config): torch.manual_seed(config['seed']) self.model = ButterflyProduct(size=config['size'], complex=True, fixed_order=config['fixed_order'], softmax_fn=config['softmax_fn']) if (not config['fixed_order']) and config['softmax_fn'] == 'softmax': self.semantic_loss_weight = config['semantic_loss_weight'] self.optimizer = optim.Adam(self.model.parameters(), lr=config['lr']) self.n_steps_per_epoch = config['n_steps_per_epoch'] size = config['size'] n = size x = np.linspace(-1, 1, n + 2)[1:-1] E = legendre.legvander(x, n - 1).T self.target_matrix = torch.tensor(E, dtype=torch.float) arange_ = np.arange(size) dct_perm = np.concatenate((arange_[::2], arange_[::-2])) br_perm = bitreversal_permutation(size) assert config['perm'] in ['id', 'br', 'dct'] if config['perm'] == 'id': self.perm = torch.arange(size) elif config['perm'] == 'br': self.perm = br_perm elif config['perm'] == 'dct': self.perm = torch.arange(size)[dct_perm][br_perm] else: assert False, 'Wrong perm in config'
Example #22
Source File: learning_legendre.py From learning-circuits with Apache License 2.0 | 5 votes |
def _setup(self, config): torch.manual_seed(config['seed']) self.model = ButterflyProduct(size=config['size'], complex=False, fixed_order=config['fixed_order'], softmax_fn=config['softmax_fn']) if (not config['fixed_order']) and config['softmax_fn'] == 'softmax': self.semantic_loss_weight = config['semantic_loss_weight'] self.optimizer = optim.Adam(self.model.parameters(), lr=config['lr']) self.n_steps_per_epoch = config['n_steps_per_epoch'] size = config['size'] # Need to transpose as dct acts on rows of matrix np.eye, not columns n = size x = np.linspace(-1, 1, n + 2)[1:-1] E = legendre.legvander(x, n - 1).T # E = np.zeros((n, n), dtype=np.float32) # for i, coef in enumerate(np.eye(n)): # E[i] = legendre.legval(x, coef) self.target_matrix = torch.tensor(E, dtype=torch.float) arange_ = np.arange(size) dct_perm = np.concatenate((arange_[::2], arange_[::-2])) br_perm = bitreversal_permutation(size) assert config['perm'] in ['id', 'br', 'dct'] if config['perm'] == 'id': self.perm = torch.arange(size) elif config['perm'] == 'br': self.perm = br_perm elif config['perm'] == 'dct': self.perm = torch.arange(size)[dct_perm][br_perm] else: assert False, 'Wrong perm in config'
Example #23
Source File: test_legendre.py From ImageFusion with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #24
Source File: test_legendre.py From mxnet-lambda with Apache License 2.0 | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #25
Source File: test_legendre.py From pySINDy with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #26
Source File: test_legendre.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #27
Source File: test_legendre.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #28
Source File: test_legendre.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #29
Source File: test_legendre.py From Computable with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)
Example #30
Source File: test_legendre.py From vnpy_crypto with MIT License | 5 votes |
def test_100(self): x, w = leg.leggauss(100) # test orthogonality. Note that the results need to be normalized, # otherwise the huge values that can arise from fast growing # functions like Laguerre can be very confusing. v = leg.legvander(x, 99) vv = np.dot(v.T * w, v) vd = 1/np.sqrt(vv.diagonal()) vv = vd[:, None] * vv * vd assert_almost_equal(vv, np.eye(100)) # check that the integral of 1 is correct tgt = 2.0 assert_almost_equal(w.sum(), tgt)