def _mul_scalar(self, other):
res_dtype = upcast_scalar(self.dtype, other)
# Multiply this scalar by every element.
new = dok_matrix(self.shape, dtype=res_dtype)
for (key, val) in iteritems(self):
new[key] = val * other
return new
def _mul_scalar(self, other):
res_dtype = upcast_scalar(self.dtype, other)
# Multiply this scalar by every element.
new = dok_matrix(self.shape, dtype=res_dtype)
for (key, val) in iteritems(self):
new[key] = val * other
return new
def transpose(self):
""" Return the transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = value
return new
def conjtransp(self):
""" Return the conjugate transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = np.conj(value)
return new
def __itruediv__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val / other
return self
else:
return NotImplemented
def transpose(self):
""" Return the transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = value
return new
def _mul_multivector(self, other):
# matrix * multivector
M, N = self.shape
n_vecs = other.shape[1] # number of column vectors
result = np.zeros((M, n_vecs), dtype=upcast(self.dtype, other.dtype))
for (i, j), v in iteritems(self):
result[i, :] += v * other[j, :]
return result
def conjtransp(self):
""" Return the conjugate transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = np.conj(value)
return new
def __itruediv__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val / other
return self
else:
return NotImplemented
def _mul_multivector(self, other):
# matrix * multivector
M,N = self.shape
n_vecs = other.shape[1] # number of column vectors
result = np.zeros((M,n_vecs), dtype=upcast(self.dtype,other.dtype))
for (i,j),v in iteritems(self):
result[i,:] += v * other[j,:]
return result
def __imul__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val * other
# new.dtype.char = self.dtype.char
return self
else:
return NotImplemented
def __imul__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val * other
# new.dtype.char = self.dtype.char
return self
else:
return NotImplemented
def __str__(self):
ss = "%s\n" % self.__class__
for key, val in iteritems(self.__dict__):
if (issubclass(self.__dict__[key].__class__, Struct)):
ss += " %s:\n %s\n" % (key, self.__dict__[key].__class__)
else:
aux = "\n" + str(val)
aux = aux.replace("\n", "\n ")
ss += " %s:\n%s\n" % (key, aux[1:])
return(ss.rstrip())
def __truediv__(self, other):
if isscalarlike(other):
new = dok_matrix(self.shape, dtype=self.dtype)
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
new[key] = val / other
#new.dtype.char = self.dtype.char
return new
else:
return self.tocsr() / other
def __str__(self):
ss = "%s\n" % self.__class__
for key, val in iteritems(self.__dict__):
if (issubclass(self.__dict__[key].__class__, Struct)):
ss += " %s:\n %s\n" % (key, self.__dict__[key].__class__)
else:
aux = "\n" + str(val)
aux = aux.replace("\n", "\n ")
ss += " %s:\n%s\n" % (key, aux[1:])
return (ss.rstrip())
def __truediv__(self, other):
if isscalarlike(other):
new = dok_matrix(self.shape, dtype=self.dtype)
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
new[key] = val / other
# new.dtype.char = self.dtype.char
return new
else:
return self.tocsr() / other
def dok_lt(self, other):
"""
Element-wise greater than
"""
# First, store all the non-trivial elements
ret = sp.dok_matrix(self, dtype = bool, copy = True)
for (key, val) in iteritems(self):
ret[key] = val < other
# TODO: dok_matrix by default returns 0.0 for unknown keys
# this should be replaced by either False for bool arrays
# OR, better, a default setting, either True or False
return ret
x = data['ENTRIESn_hourly'][data['rain'] == 0]
y = data['ENTRIESn_hourly'][data['rain'] == 1]
# As there was an issue with the scipy.stats.mannwhitneyu(y,x) which gave a nan p-value, I had to copy/paste the
# calculating functions instead of using directly scipy.stats.mannwhitneyu(x,y)
use_continuity = True
x = ma.asarray(x).compressed().view(ndarray)
y = ma.asarray(y).compressed().view(ndarray)
ranks = rankdata(np.concatenate([x,y]))
(nx, ny) = (len(x), len(y))
nt = nx + ny
U = ranks[:nx].sum() - nx*(nx+1)/2.
U = max(U, nx*ny - U)
u = nx*ny - U
#
mu = (nx*ny)/2.
sigsq = (nt**3 - nt)/12.
ties = count_tied_groups(ranks)
sigsq -= np.sum(v*(k**3-k) for (k,v) in iteritems(ties))/12.
sigsq *= nx*ny/float(nt*(nt-1))
if use_continuity:
z = (U - 1/2. - mu) / ma.sqrt(sigsq)
else:
z = (U - mu) / ma.sqrt(sigsq)
prob = special.erfc(abs(z)/np.sqrt(2))
print "p-value : " + str(prob)
def _mul_vector(self, other):
# matrix * vector
result = np.zeros(self.shape[0], dtype=upcast(self.dtype, other.dtype))
for (i, j), v in iteritems(self):
result[i] += v * other[j]
return result
def _mul_vector(self, other):
# matrix * vector
result = np.zeros(self.shape[0], dtype=upcast(self.dtype,other.dtype))
for (i,j),v in iteritems(self):
result[i] += v * other[j]
return result
