本文整理汇总了Python中scipy.newaxis方法的典型用法代码示例。如果您正苦于以下问题:Python scipy.newaxis方法的具体用法?Python scipy.newaxis怎么用?Python scipy.newaxis使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy
的用法示例。
在下文中一共展示了scipy.newaxis方法的9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: coupling_optim_garrick
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def coupling_optim_garrick(y,t):
creation=s.zeros(n_bin)
destruction=s.zeros(n_bin)
#now I try to rewrite this in a more optimized way
destruction = -s.dot(s.transpose(kernel),y)*y #much more concise way to express\
#the destruction of k-mers
for k in xrange(n_bin):
kyn = (kernel*f_garrick[:,:,k])*y[:,s.newaxis]*y[s.newaxis,:]
creation[k] = s.sum(kyn)
creation=0.5*creation
out=creation+destruction
return out
#Now I work with the function for espressing smoluchowski equation when a uniform grid is used
示例2: coupling_optim
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def coupling_optim(y,t):
creation=s.zeros(n_bin)
destruction=s.zeros(n_bin)
#now I try to rewrite this in a more optimized way
destruction = -s.dot(s.transpose(kernel),y)*y #much more concise way to express\
#the destruction of k-mers
kyn = kernel*y[:,s.newaxis]*y[s.newaxis,:]
for k in xrange(n_bin):
creation[k] = s.sum(kyn[s.arange(k),k-s.arange(k)-1])
creation=0.5*creation
out=creation+destruction
return out
#Now I go for the optimal optimization of the chi_{i,j,k} coefficients used by Garrick for
# dealing with a non-uniform grid.
示例3: appendToHDF5
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def appendToHDF5(file, data, name):
### get current shape
tmp = file[name].shape
### resize
if len(tmp) == 1:
file[name].resize((tmp[0] + data.shape[0],))
file[name][tmp[0]:] = data
elif len(tmp) == 2:
file[name].resize((tmp[0], tmp[1] + 1))
if len(data.shape) < 2:
file[name][:, tmp[1]:] = data[:, sp.newaxis]
else:
file[name][:, tmp[1]:] = data
else:
print >> sys.stderr, "cannot append data to HDF5 with more than 2 dimensions"
sys.exit(-1)
示例4: __quadratic_forms_matrix_euclidean
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def __quadratic_forms_matrix_euclidean(h1, h2):
r"""
Compute the bin-similarity matrix for the quadratic form distance measure.
The matric :math:`A` for two histograms :math:`H` and :math:`H'` of size :math:`m` and
:math:`n` respectively is defined as
.. math::
A_{m,n} = 1 - \frac{d_2(H_m, {H'}_n)}{d_{max}}
with
.. math::
d_{max} = \max_{m,n}d_2(H_m, {H'}_n)
See also
--------
quadratic_forms
"""
A = scipy.repeat(h2[:,scipy.newaxis], h1.size, 1) # repeat second array to form a matrix
A = scipy.absolute(A - h1) # euclidean distances
return 1 - (A / float(A.max()))
# //////////////// #
# Helper functions #
# //////////////// #
示例5: Brow_ker_cont_optim
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def Brow_ker_cont_optim(Vlist):
kern_mat=2.*k_B*T_0/(3.*mu)*(Vlist[:,s.newaxis]**(1./3.)+\
Vlist[s.newaxis,:]**(1./3.))**2./ \
(Vlist[:,s.newaxis]**(1./3.)*Vlist[s.newaxis,:]**(1./3.))
return kern_mat
示例6: mycount_garrick
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def mycount_garrick(V):
f=s.zeros((n_bin, n_bin, n_bin))
Vsum=V[:,s.newaxis]+V[s.newaxis,:] # matrix with the sum of the volumes in the bins
for k in xrange(1,(n_bin-1)):
f[:,:,k]=s.where((Vsum<=V[k+1]) & (Vsum>=V[k]), (V[k+1]-Vsum)/(V[k+1]-V[k]),\
f[:,:,k] )
f[:,:,k]=s.where((Vsum<=V[k]) & (Vsum>=V[k-1]),(Vsum-V[k-1])/(V[k]-V[k-1]),\
f[:,:,k])
return f
示例7: plot_llk
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def plot_llk(train_elbo, test_elbo):
import matplotlib.pyplot as plt
import scipy as sp
import seaborn as sns
import pandas as pd
plt.figure(figsize=(30, 10))
sns.set_style("whitegrid")
data = np.concatenate([np.arange(len(test_elbo))[:, sp.newaxis], -test_elbo[:, sp.newaxis]], axis=1)
df = pd.DataFrame(data=data, columns=['Training Epoch', 'Test ELBO'])
g = sns.FacetGrid(df, size=10, aspect=1.5)
g.map(plt.scatter, "Training Epoch", "Test ELBO")
g.map(plt.plot, "Training Epoch", "Test ELBO")
plt.savefig(str(Path(result_dir, 'test_elbo_vae.png')))
plt.close('all')
示例8: set_sparse_diag_to_one
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def set_sparse_diag_to_one(mat):
# appears to implicitly convert to csr which might be a problem
(n, n) = mat.shape
# copy the matrix, subtract the diagonal values, add identity matrix
# see http://nbviewer.jupyter.org/gist/Midnighter/9992103 for speed testing
cpy = mat - dia_matrix((mat.diagonal()[sp.newaxis, :], [0]), shape=(n, n)) + identity(n)
return(cpy)
示例9: read_face_data
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import newaxis [as 别名]
def read_face_data(h5fn):
f = h5py.File(h5fn, "r")
keys = ["test", "train", "val"]
Y = {}
Rid = {}
Did = {}
for key in keys:
Y[key] = f["Y_" + key][:]
for key in keys:
Rid[key] = f["Rid_" + key][:]
for key in keys:
Did[key] = f["Did_" + key][:]
f.close()
# exclude test and validation not in trian
uDid = sp.unique(Did["train"])
for key in ["test", "val"]:
Iok = sp.in1d(Did[key], uDid)
Y[key] = Y[key][Iok]
Rid[key] = Rid[key][Iok]
Did[key] = Did[key][Iok]
# one hot encode donors
table = {}
for _i, _id in enumerate(uDid):
table[_id] = _i
D = {}
for key in keys:
D[key] = sp.array([table[_id] for _id in Did[key]])[:, sp.newaxis]
# one hot encode views
uRid = sp.unique(sp.concatenate([Rid[key] for key in keys]))
table_w = {}
for _i, _id in enumerate(uRid):
table_w[_id] = _i
W = {}
for key in keys:
W[key] = sp.array([table_w[_id] for _id in Rid[key]])[:, sp.newaxis]
for key in keys:
Y[key] = Y[key].astype(float) / 255.0
Y[key] = torch.tensor(Y[key].transpose((0, 3, 1, 2)).astype(sp.float32))
D[key] = torch.tensor(D[key].astype(sp.float32))
W[key] = torch.tensor(W[key].astype(sp.float32))
return Y, D, W