本文整理汇总了Python中scipy.empty方法的典型用法代码示例。如果您正苦于以下问题:Python scipy.empty方法的具体用法?Python scipy.empty怎么用?Python scipy.empty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.empty方法的5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: fake_mldata
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import empty [as 别名]
def fake_mldata(columns_dict, dataname, matfile, ordering=None):
"""Create a fake mldata data set.
.. deprecated:: 0.20
Will be removed in version 0.22
Parameters
----------
columns_dict : dict, keys=str, values=ndarray
Contains data as columns_dict[column_name] = array of data.
dataname : string
Name of data set.
matfile : string or file object
The file name string or the file-like object of the output file.
ordering : list, default None
List of column_names, determines the ordering in the data set.
Notes
-----
This function transposes all arrays, while fetch_mldata only transposes
'data', keep that into account in the tests.
"""
datasets = dict(columns_dict)
# transpose all variables
for name in datasets:
datasets[name] = datasets[name].T
if ordering is None:
ordering = sorted(list(datasets.keys()))
# NOTE: setting up this array is tricky, because of the way Matlab
# re-packages 1D arrays
datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
dtype='object')
for i, name in enumerate(ordering):
datasets['mldata_descr_ordering'][0, i] = name
scipy.io.savemat(matfile, datasets, oned_as='column')
示例2: fake_mldata
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import empty [as 别名]
def fake_mldata(columns_dict, dataname, matfile, ordering=None):
"""Create a fake mldata data set.
Parameters
----------
columns_dict : dict, keys=str, values=ndarray
Contains data as columns_dict[column_name] = array of data.
dataname : string
Name of data set.
matfile : string or file object
The file name string or the file-like object of the output file.
ordering : list, default None
List of column_names, determines the ordering in the data set.
Notes
-----
This function transposes all arrays, while fetch_mldata only transposes
'data', keep that into account in the tests.
"""
datasets = dict(columns_dict)
# transpose all variables
for name in datasets:
datasets[name] = datasets[name].T
if ordering is None:
ordering = sorted(list(datasets.keys()))
# NOTE: setting up this array is tricky, because of the way Matlab
# re-packages 1D arrays
datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
dtype='object')
for i, name in enumerate(ordering):
datasets['mldata_descr_ordering'][0, i] = name
scipy.io.savemat(matfile, datasets, oned_as='column')
示例3: parse_plink_snps
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import empty [as 别名]
def parse_plink_snps(genotype_file, snp_indices):
plinkf = plinkfile.PlinkFile(genotype_file)
samples = plinkf.get_samples()
num_individs = len(samples)
num_snps = len(snp_indices)
raw_snps = sp.empty((num_snps, num_individs), dtype='int8')
# If these indices are not in order then we place them in the right place while parsing SNPs.
snp_order = sp.argsort(snp_indices)
ordered_snp_indices = list(snp_indices[snp_order])
ordered_snp_indices.reverse()
# Iterating over file to load SNPs
snp_i = 0
next_i = ordered_snp_indices.pop()
line_i = 0
max_i = ordered_snp_indices[0]
while line_i <= max_i:
if line_i < next_i:
next(plinkf)
elif line_i == next_i:
line = next(plinkf)
snp = sp.array(line, dtype='int8')
bin_counts = line.allele_counts()
if bin_counts[-1] > 0:
mode_v = sp.argmax(bin_counts[:2])
snp[snp == 3] = mode_v
s_i = snp_order[snp_i]
raw_snps[s_i] = snp
if line_i < max_i:
next_i = ordered_snp_indices.pop()
snp_i += 1
line_i += 1
plinkf.close()
assert snp_i == len(raw_snps), 'Parsing SNPs from plink file failed.'
num_indivs = len(raw_snps[0])
freqs = sp.sum(raw_snps, 1, dtype='float32') / (2 * float(num_indivs))
return raw_snps, freqs
示例4: csr_to_problem
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import empty [as 别名]
def csr_to_problem(x, prob):
# Extra space for termination node and (possibly) bias term
x_space = prob.x_space = scipy.empty((x.nnz+x.shape[0]*2), dtype=feature_node)
prob.rowptr = x.indptr.copy()
prob.rowptr[1:] += 2*scipy.arange(1,x.shape[0]+1)
prob_ind = x_space["index"]
prob_val = x_space["value"]
prob_ind[:] = -1
if jit_enabled:
csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr)
else:
csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr)
示例5: ldpred_inf
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import empty [as 别名]
def ldpred_inf(beta_hats, h2=0.1, n=1000, inf_shrink_matrices=None,
reference_ld_mats=None, genotypes=None, ld_window_size=100, verbose=False):
"""
Apply the infinitesimal shrink w LD (which requires LD information).
If reference_ld_mats are supplied, it uses those, otherwise it uses the LD in the genotype data.
If genotypes are supplied, then it assumes that beta_hats and the genotypes are synchronized.
"""
n = float(n)
if verbose:
print('Doing LD correction')
t0 = time.time()
m = len(beta_hats)
updated_betas = sp.empty(m)
for i, wi in enumerate(range(0, m, ld_window_size)):
start_i = wi
stop_i = min(m, wi + ld_window_size)
curr_window_size = stop_i - start_i
if inf_shrink_matrices!=None:
A_inv = inf_shrink_matrices[i]
else:
if reference_ld_mats != None:
D = reference_ld_mats[i]
else:
if genotypes != None:
X = genotypes[start_i: stop_i]
num_indivs = X.shape[1]
D = sp.dot(X, X.T) / num_indivs
else:
raise NotImplementedError
A = ((m / h2) * sp.eye(curr_window_size) + (n / (1.0)) * D)
A_inv = linalg.pinv(A)
updated_betas[start_i: stop_i] = sp.dot(A_inv * n , beta_hats[start_i: stop_i]) # Adjust the beta_hats
if verbose:
sys.stdout.write('\r%0.2f%%' % (100.0 * (min(1, float(wi + ld_window_size) / m))))
sys.stdout.flush()
t1 = time.time()
t = (t1 - t0)
if verbose:
print('\nIt took %d minutes and %0.2f seconds to perform the Infinitesimal LD shrink' % (t / 60, t % 60))
return updated_betas