The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def set_ufunc(self, scalar_op):
# This is probably a speed up of the implementation
if isinstance(scalar_op, theano.scalar.basic.Add):
self.ufunc = numpy.add
elif isinstance(scalar_op, theano.scalar.basic.Mul):
self.ufunc = numpy.multiply
elif isinstance(scalar_op, theano.scalar.basic.Maximum):
self.ufunc = numpy.maximum
elif isinstance(scalar_op, theano.scalar.basic.Minimum):
self.ufunc = numpy.minimum
elif isinstance(scalar_op, theano.scalar.basic.AND):
self.ufunc = numpy.bitwise_and
elif isinstance(scalar_op, theano.scalar.basic.OR):
self.ufunc = numpy.bitwise_or
elif isinstance(scalar_op, theano.scalar.basic.XOR):
self.ufunc = numpy.bitwise_xor
else:
self.ufunc = numpy.frompyfunc(scalar_op.impl, 2, 1) Example 2
def _KeenerMatrix(self, A, C, regularization, func, epsilon):
"""func is a regularization function imposed on every element of matrix.
"""
# Apply Laplace Law
B = A+A.T+2;
A = A+1
A = A/B
# Regularization
if func is not None:
h = np.frompyfunc(func, 1, 1)
A = np.require(h(A), dtype=np.float32)
# divide by contest number
C = C+C.T
c = np.sum(C, axis=1)
if regularization:
A = A/np.expand_dims(c, axis=1)
A[C==0]=0
if epsilon is not None:
A += epsilon*np.ones(A.shape, A.dtype)
return A Example 3
def ztnb_pmf(y, mu, alpha):
r = 1.0 / alpha
if y <= 0:
raise Exception('y must be larger than 0.')
p = mu/(mu+r+0.0)
ztnbin_mpmath = lambda y, p, r: mpmath.gamma(y + r)/(mpmath.gamma(y+1)*mpmath.gamma(r))*np.power(1-p, r)*np.power(p, y)/(1-np.power(1-p, r))
ztnbin = np.frompyfunc(ztnbin_mpmath, 3, 1)
return float(ztnbin(y, p, r)) Example 4
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 5
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 6
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 7
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 8
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 9
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 10
def _maybe_convert(values, val_kind, encoding):
if _need_convert(val_kind):
conv = _get_converter(val_kind, encoding)
# conv = np.frompyfunc(conv, 1, 1)
values = conv(values)
return values Example 11
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 12
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 13
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 14
def draw_mandelbrot(cx, cy, d):
"""
???(cx, cy)????d????Mandelbrot
"""
x0, x1, y0, y1 = cx-d, cx+d, cy-d, cy+d
y, x = np.ogrid[y0:y1:200j, x0:x1:200j]
c = x + y*1j
start = time.clock()
mandelbrot = np.frompyfunc(iter_point,1,1)(c).astype(np.float)
print("time=",time.clock() - start)
pl.imshow(mandelbrot, cmap=cm.jet, extent=[x0,x1,y0,y1])
#pl.gca().set_axis_off() Example 15
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 16
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 17
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 18
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 19
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 20
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 21
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 22
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 23
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 24
def __setstate__(self, d):
super(Elemwise, self).__setstate__(d)
self.ufunc = None
self.nfunc = None
if getattr(self, 'nfunc_spec', None):
self.nfunc = getattr(numpy, self.nfunc_spec[0])
elif 0 < self.scalar_op.nin < 32:
self.ufunc = numpy.frompyfunc(self.scalar_op.impl,
self.scalar_op.nin,
self.scalar_op.nout)
self._rehash() Example 25
def compute_optimal_scales(self):
"""Form a set of scales to use in the wavelet transform.
For non-orthogonal wavelet analysis, one can use an
arbitrary set of scales.
It is convenient to write the scales as fractional powers of
two:
s_j = s_0 * 2 ** (j * dj), j = 0, 1, ..., J
J = (1 / dj) * log2(N * dt / s_0)
s0 - smallest resolvable scale
J - largest scale
choose s0 so that the equivalent Fourier period is 2 * dt.
The choice of dj depends on the width in spectral space of
the wavelet function. For the morlet, dj=0.5 is the largest
that still adequately samples scale. Smaller dj gives finer
scale resolution.
"""
dt = self.dt
# resolution
dj = self.dj
# smallest resolvable scale, chosen so that the equivalent
# fourier period is approximately 2dt
s0 = self.s0
# Largest scale
J = int((1 / dj) * np.log2(self.N * dt / s0))
sj = s0 * 2 ** (dj * np.arange(0, J + 1))
return sj
# TODO: use np.frompyfunc on this
# TODO: can we just replace it with fftfreqs? Example 26
def compute_optimal_scales(self):
"""Form a set of scales to use in the wavelet transform.
For non-orthogonal wavelet analysis, one can use an
arbitrary set of scales.
It is convenient to write the scales as fractional powers of
two:
s_j = s_0 * 2 ** (j * dj), j = 0, 1, ..., J
J = (1 / dj) * log2(N * dt / s_0)
s0 - smallest resolvable scale
J - largest scale
choose s0 so that the equivalent Fourier period is 2 * dt.
The choice of dj depends on the width in spectral space of
the wavelet function. For the morlet, dj=0.5 is the largest
that still adequately samples scale. Smaller dj gives finer
scale resolution.
"""
dt = self.dt
# resolution
dj = self.dj
# smallest resolvable scale, chosen so that the equivalent
# fourier period is approximately 2dt
s0 = self.s0
# Largest scale
J = int((1 / dj) * np.log2(self.N * dt / s0))
sj = s0 * 2 ** (dj * np.arange(0, J + 1))
return sj
# TODO: use np.frompyfunc on this
# TODO: can we just replace it with fftfreqs? Example 27
def test_frompyfunc_endian(self, level=rlevel):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float)) Example 28
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1) Example 29
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]]) Example 30
def __init__(self, p = 0.1, r = 10): nbin_mpmath = lambda k, p, r: mpmath.gamma(k + r)/(mpmath.gamma(k+1)\ *mpmath.gamma(r))*np.power(1-p, r)*np.power(p, k) self.nbin = np.frompyfunc(nbin_mpmath, 3, 1) self.p = p self.r = r
Example 31
def transform(self,X):
"""
?????: ???0?n-1???????????-1?
X: ?????????DataFrame??Series?
???????????????????DataFrame??Series?
"""
data=X.copy()
if isinstance(data,np.ndarray):
if isinstance(self.fill_na,str):
raise Exception('numpy?????????????')
if not self.return_numeric:
warnings.warn('numpy????????????????????????dataframe?series?')
if not self.return_numeric:
newlabel=self.get_label()
if len(data.shape)==1:
tmp=np.searchsorted(self.cuts,data).astype(int)
result=np.where(np.isnan(data),-1,tmp)
if (not self.return_numeric) and (not isinstance(data,np.ndarray)):
f=np.frompyfunc(lambda xx: newlabel.get(xx,self.fill_na),1,1)
result=f(result)
if isinstance(data,np.ndarray):
result[result==-1]=self.fill_na
else:
result=pd.Series(result)
result.index=data.index
result.index.name=data.index.name
result.name=data.name
result[result==-1]=self.fill_na
data=result.copy()
else:
for feature in self.cuts:
if not isinstance(data,pd.DataFrame):
tmp=np.searchsorted(self.cuts[feature],data[:,feature]).astype(int)
data[:,feature]=np.where(np.isnan(data[:,feature]),self.fill_na,tmp)
else:
tmp=np.searchsorted(self.cuts[feature],data[feature]).astype(int)
data[feature]=np.where(np.isnan(data[feature]),-1,tmp)
if not self.return_numeric:
f=np.frompyfunc(lambda xx: newlabel[feature].get(xx,self.fill_na),1,1)
data[feature]=f(data[feature])
else:
data.loc[data[feature]==-1,feature]=self.fill_na
if self.return_array and isinstance(data,(pd.Series,pd.DataFrame)):
return data.values
else:
return data Example 32
def prepare_node(self, node, storage_map, compute_map, impl):
# Postpone the ufunc building to the last minutes
# NumPy ufunc support only up to 31 inputs.
# But our c code support more.
if (len(node.inputs) < 32 and
(self.nfunc is None or
self.scalar_op.nin != len(node.inputs)) and
self.ufunc is None and
impl == 'py'):
ufunc = numpy.frompyfunc(self.scalar_op.impl,
len(node.inputs),
self.scalar_op.nout)
if self.scalar_op.nin > 0:
# We can reuse it for many nodes
self.ufunc = ufunc
else:
node.tag.ufunc = ufunc
# Numpy ufuncs will sometimes perform operations in
# float16, in particular when the input is int8.
# This is not something that we want, and we do not
# do it in the C code, so we specify that the computation
# should be carried out in the returned dtype.
# This is done via the "sig" kwarg of the ufunc, its value
# should be something like "ff->f", where the characters
# represent the dtype of the inputs and outputs.
# NumPy 1.10.1 raise an error when giving the signature
# when the input is complex. So add it only when inputs is int.
out_dtype = node.outputs[0].dtype
if (out_dtype in float_dtypes and
isinstance(self.nfunc, numpy.ufunc) and
node.inputs[0].dtype in discrete_dtypes):
char = numpy.sctype2char(out_dtype)
sig = char * node.nin + '->' + char * node.nout
node.tag.sig = sig
node.tag.fake_node = Apply(
self.scalar_op,
[get_scalar_type(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[get_scalar_type(dtype=output.type.dtype).make_variable()
for output in node.outputs])
self.scalar_op.prepare_node(node.tag.fake_node, None, None, impl) Example 33
def perform(self, node, inp, out):
input, = inp
output, = out
axis = self.axis
if axis is None:
axis = list(range(input.ndim))
variable = input
to_reduce = reversed(sorted(axis))
if hasattr(self, 'acc_dtype') and self.acc_dtype is not None:
acc_dtype = self.acc_dtype
else:
acc_dtype = node.outputs[0].type.dtype
if to_reduce:
for dimension in to_reduce:
# If it's a zero-size array, use scalar_op.identity
# if available
if variable.shape[dimension] == 0:
if hasattr(self.scalar_op, 'identity'):
# Compute the shape of the output
v_shape = list(variable.shape)
del v_shape[dimension]
variable = numpy.empty(tuple(v_shape),
dtype=acc_dtype)
variable.fill(self.scalar_op.identity)
else:
raise ValueError((
"Input (%s) has zero-size on axis %s, but "
"self.scalar_op (%s) has no attribute 'identity'"
% (variable, dimension, self.scalar_op)))
else:
# Numpy 1.6 has a bug where you sometimes have to specify
# "dtype='object'" in reduce for it to work, if the ufunc
# was built with "frompyfunc". We need to find out if we
# are in one of these cases (only "object" is supported in
# the output).
if ((self.ufunc.ntypes == 1) and
(self.ufunc.types[0][-1] == 'O')):
variable = self.ufunc.reduce(variable, dimension,
dtype='object')
else:
variable = self.ufunc.reduce(variable, dimension,
dtype=acc_dtype)
variable = numpy.asarray(variable)
if numpy.may_share_memory(variable, input):
# perhaps numpy is clever for reductions of size 1?
# We don't want this.
variable = variable.copy()
output[0] = theano._asarray(variable,
dtype=node.outputs[0].type.dtype)
else:
# Force a copy
output[0] = numpy.array(variable, copy=True,
dtype=node.outputs[0].type.dtype) 