def test_basic(self):
x = zvector()
rng = np.random.RandomState(23)
xval = np.asarray(
list(np.complex(rng.randn(), rng.randn()) for i in range(10)))
assert np.all(xval.real == theano.function([x], real(x))(xval))
assert np.all(xval.imag == theano.function([x], imag(x))(xval))
import numpy as np
import scipy.signal as ss
import argparse
import theano
import theano.tensor as T
# Theano functions
# QEA function
x0, x1, x2 = (T.zvector('x0'),T.zvector('x1'), T.zvector('x2'))
IF_FUN = T.arccos((x2 + x0) / (2 * x1 + 1e-16)) / np.pi / 2
QEA_FUN = theano.function([x0, x1, x2], IF_FUN)
def floatx(np_data):
return np.asarray(np_data, dtype=theano.config.floatX)
def qea(im):
"""
Quasi-eigen approximation function
Input
- im : 1d vector that contains a time series
Ouput
- ia : instantaneous amplitude
- ip : instantaneous phase
- ifeq: instantaneous frequency
"""
im = floatx(im.ravel())
# computes analytic signal
H = ss.hilbert(im)
H = im+1j*H
def test_cast(self):
x = zvector()
with pytest.raises(TypeError):
cast(x, "int32")
import numpy as np
import theano as th
import theano.tensor as tt
# th.config.device='gpu'
# X = tt.dscalar('X')
# y = tt.dscalar('y')
# z = X + y
# f = th.function([X, y], z)
# f(2,3)
# np.allclose(f(16.3, 12.1), 28.4)
X = tt.zvector('X')
y = tt.zvector('y')
m = tt.dmatrix('m')
n = tt.dmatrix('n')
zout = tt.dot(m, n)
P = th.function([m, n], zout)
s_outputs, s_updates = th.scan(fn=lambda i, j: tt.dot(i, j))
P = th.function(inputs=[m, n], outputs=s_outputs, updates=s_updates)
M = np.array(3, dtype=th.config.floatX)
N = np.array(4, dtype=th.config.floatX)
P(M, N)