本文整理汇总了Python中numpy.geomspace方法的典型用法代码示例。如果您正苦于以下问题:Python numpy.geomspace方法的具体用法?Python numpy.geomspace怎么用?Python numpy.geomspace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numpy
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在下文中一共展示了numpy.geomspace方法的15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: test_celer_path_logreg
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def test_celer_path_logreg():
X, y = build_dataset(
n_samples=50, n_features=100, sparse_X=True)
y = np.sign(y)
alpha_max = norm(X.T.dot(y), ord=np.inf) / 2
alphas = alpha_max * np.geomspace(1, 1e-2, 10)
tol = 1e-8
coefs, Cs, n_iters = _logistic_regression_path(
X, y, Cs=1. / alphas, fit_intercept=False, penalty='l1',
solver='liblinear', tol=tol)
_, coefs_c, gaps = celer_path(
X, y, "logreg", alphas=alphas, tol=tol, verbose=2)
np.testing.assert_array_less(gaps, tol)
np.testing.assert_allclose(coefs != 0, coefs_c.T != 0)
np.testing.assert_allclose(coefs, coefs_c.T, atol=1e-5, rtol=1e-3)
示例2: test_scott_vs_stone
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def test_scott_vs_stone(self):
"""Verify that Scott's rule and Stone's rule converges for normally distributed data"""
def nbins_ratio(seed, size):
rng = np.random.RandomState(seed)
x = rng.normal(loc=0, scale=2, size=size)
a, b = len(np.histogram(x, 'stone')[0]), len(np.histogram(x, 'scott')[0])
return a / (a + b)
ll = [[nbins_ratio(seed, size) for size in np.geomspace(start=10, stop=100, num=4).round().astype(int)]
for seed in range(256)]
# the average difference between the two methods decreases as the dataset size increases.
assert_almost_equal(abs(np.mean(ll, axis=0) - 0.5),
[0.1065248,
0.0968844,
0.0331818,
0.0178057],
decimal=3)
示例3: next
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def next(self, state, **runopts):
bqm = state.problem
# get a reasonable beta range
beta_hot, beta_cold = neal.default_beta_range(bqm)
# generate betas for all branches/replicas
betas = np.geomspace(beta_hot, beta_cold, self.num_replicas)
# create num_replicas with betas spaced with geometric progression
states = hybrid.States(*[state.updated(beta=b) for b in betas])
return states
#
# A few PT workflow generators. Should be treated as Runnable classes
#
示例4: next
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def next(self, state, **runopts):
bqm = state.problem
# get a reasonable beta range
beta_hot, beta_cold = neal.default_beta_range(bqm)
# generate betas
if self.interpolation == 'linear':
beta_schedule = np.linspace(beta_hot, beta_cold, self.length)
elif self.interpolation == 'geometric':
beta_schedule = np.geomspace(beta_hot, beta_cold, self.length)
else:
raise ValueError("Beta schedule type {} not implemented".format(self.interpolation))
# store the schedule in output state
return state.updated(beta_schedule=beta_schedule)
示例5: geomspace
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def geomspace(start, stop, num=50, endpoint=True, dtype=float): # pylint: disable=missing-docstring
if dtype:
dtype = utils.result_type(dtype)
if num < 0:
raise ValueError('Number of samples {} must be non-negative.'.format(num))
if not num:
return empty([0])
step = 1.
if endpoint:
if num > 1:
step = tf.pow((stop / start), 1 / (num - 1))
else:
step = tf.pow((stop / start), 1 / num)
result = tf.cast(tf.range(num), step.dtype)
result = tf.pow(step, result)
result = tf.multiply(result, start)
if dtype:
result = tf.cast(result, dtype=dtype)
return arrays_lib.tensor_to_ndarray(result)
# Building matrices.
示例6: testGeomSpace
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def testGeomSpace(self):
def run_test(start, stop, **kwargs):
arg1 = start
arg2 = stop
self.match(
array_ops.geomspace(arg1, arg2, **kwargs),
np.geomspace(arg1, arg2, **kwargs),
msg='geomspace({}, {})'.format(arg1, arg2),
almost=True,
decimal=4)
run_test(1, 1000, num=5)
run_test(1, 1000, num=5, endpoint=False)
run_test(-1, -1000, num=5)
run_test(-1, -1000, num=5, endpoint=False)
示例7: monkeypatch
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def monkeypatch():
"""
monkeypatch built-in numpy functions to call those provided by nparray instead.
```py
import nparray
import numpy as np
nparray.monkeypatch()
print(np.linspace(0,1,11))
```
"""
np.array = array
np.arange = arange
np.linspace = linspace
np.logspace = logspace
np.geomspace = geomspace
np.full = full
np.full_like = full_like
np.zeros = zeros
np.zeros_like = zeros_like
np.ones = ones
np.ones_like = ones_like
np.eye = eye
示例8: generate_combo
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def generate_combo(self, params_dict):
"""
Method to generate all combinations from a given set of key-value pairs
Args:
params_dict: Set of key-value pairs with the key being the param name and the value being the list of values
you want to try for that param
Returns:
new_dict: The list of all combinations of parameters
"""
if not params_dict:
return None
new_dict = {}
for key, value in params_dict.items():
assert isinstance(value, collections.Iterable)
if key == 'layers':
new_dict[key] = value
elif type(value[0]) != str:
tmp_list = list(np.geomspace(value[0], value[1], value[2]))
if key in self.convert_to_int:
new_dict[key] = [int(x) for x in tmp_list]
else:
new_dict[key] = tmp_list
else:
new_dict[key] = value
return new_dict
示例9: testGeomspace
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def testGeomspace(self, start_shape, stop_shape, num,
endpoint, dtype, rng_factory):
rng = rng_factory()
# relax default tolerances slightly
tol = {onp.float16: 4e-3, onp.float32: 2e-3, onp.complex128: 1e-14}
def args_maker():
"""Test the set of inputs onp.geomspace is well-defined on."""
start, stop = self._GetArgsMaker(rng,
[start_shape, stop_shape],
[dtype, dtype])()
# onp.geomspace can't handle differently ranked tensors
# w. negative numbers!
start, stop = lnp.broadcast_arrays(start, stop)
if dtype in complex_dtypes:
return start, stop
# to avoid NaNs, non-complex start and stop cannot
# differ in sign, elementwise
start = start * lnp.sign(start) * lnp.sign(stop)
return start, stop
start, stop = args_maker()
ndim = len(onp.shape(start + stop))
for axis in range(-ndim, ndim):
def lnp_op(start, stop):
return lnp.geomspace(start, stop, num, endpoint=endpoint, dtype=dtype,
axis=axis)
def onp_op(start, stop):
start = start.astype(onp.float32) if dtype == lnp.bfloat16 else start
stop = stop.astype(onp.float32) if dtype == lnp.bfloat16 else stop
return onp.geomspace(
start, stop, num, endpoint=endpoint,
dtype=dtype if dtype != lnp.bfloat16 else onp.float32,
axis=axis).astype(dtype)
self._CheckAgainstNumpy(onp_op, lnp_op, args_maker,
check_dtypes=False, tol=tol)
if dtype in (inexact_dtypes + [None,]):
self._CompileAndCheck(lnp_op, args_maker,
check_dtypes=False, atol=tol, rtol=tol,
check_incomplete_shape=True)
示例10: _lasso
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def _lasso(self):
"""Order features according to their corresponding coefficients."""
if self.line_search:
pred = None
try:
alpha_list = np.geomspace(self.max_alpha, self.min_alpha,
self.steps)
except AttributeError:
alpha_list = np.exp(np.linspace(np.log(self.max_alpha),
np.log(self.min_alpha),
self.steps))
for alpha in alpha_list:
regr = Lasso(alpha=alpha, max_iter=self.iter,
fit_intercept=True, normalize=True,
selection='random')
model = regr.fit(self.train_matrix, self.train_target)
nz = len(model.coef_) - (model.coef_ == 0.).sum()
if nz >= self.size:
coeff = model.coef_
break
else:
regr = LassoCV(fit_intercept=True, normalize=True,
n_alphas=self.steps, max_iter=self.iter,
eps=self.eps, cv=None)
model = regr.fit(X=self.train_matrix, y=self.train_target)
coeff = model.coef_
# Make the linear prediction.
pred = None
if self.predict:
data = model.predict(self.test_matrix)
pred = get_error(prediction=data,
target=self.test_target)['average']
return coeff, pred
示例11: preceding_sent_weighed_similarity
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def preceding_sent_weighed_similarity(self, sent_list,
item,
k=5,
start=1.1,
end=0.8,
*args):
k = min(len(sent_list), k)
return np.arange(k), np.geomspace(start, end, k)
示例12: bin_dataframe
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def bin_dataframe(df, n_bins):
"""
Assign a "bin" column to the dataframe to indicate which bin the true
charges belong to.
Bins are assigned in log space.
Parameters
----------
df : pd.DataFrame
n_bins : int
Number of bins to allow in range
Returns
-------
pd.DataFrame
"""
true = df["true"].values
min_ = true.min()
max_ = true.max()
bins = np.geomspace(min_, max_, n_bins)
log_bin_width = np.diff(np.log10(bins))[0]
bins = np.append(bins, 10 ** (np.log10(bins[-1]) + log_bin_width))
df["bin"] = np.digitize(true, bins, right=True) - 1
return df
示例13: get_length
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def get_length(matrix):
if matrix.IDENTIFIER == V1HpChoice.IDENTIFIER:
return len(matrix.value)
if matrix.IDENTIFIER == V1HpPChoice.IDENTIFIER:
return len(matrix.value)
if matrix.IDENTIFIER == V1HpRange.IDENTIFIER:
return len(np.arange(**matrix.value))
if matrix.IDENTIFIER == V1HpLinSpace.IDENTIFIER:
return len(np.linspace(**matrix.value))
if matrix.IDENTIFIER == V1HpLogSpace.IDENTIFIER:
return len(np.logspace(**matrix.value))
if matrix.IDENTIFIER == V1HpGeomSpace.IDENTIFIER:
return len(np.geomspace(**matrix.value))
if matrix.IDENTIFIER in {
V1HpUniform.IDENTIFIER,
V1HpQUniform.IDENTIFIER,
V1HpLogUniform.IDENTIFIER,
V1HpQLogUniform.IDENTIFIER,
V1HpNormal.IDENTIFIER,
V1HpQNormal.IDENTIFIER,
V1HpLogNormal.IDENTIFIER,
V1HpQLogNormal.IDENTIFIER,
}:
raise ValidationError("Distribution should not call `length`")
示例14: to_numpy
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def to_numpy(matrix):
if matrix.IDENTIFIER == V1HpChoice.IDENTIFIER:
return matrix.value
if matrix.IDENTIFIER == V1HpPChoice.IDENTIFIER:
raise ValidationError(
"Distribution should not call `to_numpy`, "
"instead it should call `sample`."
)
if matrix.IDENTIFIER == V1HpRange.IDENTIFIER:
return np.arange(**matrix.value)
if matrix.IDENTIFIER == V1HpLinSpace.IDENTIFIER:
return np.linspace(**matrix.value)
if matrix.IDENTIFIER == V1HpLogSpace.IDENTIFIER:
return np.logspace(**matrix.value)
if matrix.IDENTIFIER == V1HpGeomSpace.IDENTIFIER:
return np.geomspace(**matrix.value)
if matrix.IDENTIFIER in {
V1HpUniform.IDENTIFIER,
V1HpQUniform.IDENTIFIER,
V1HpLogUniform.IDENTIFIER,
V1HpQLogUniform.IDENTIFIER,
V1HpNormal.IDENTIFIER,
V1HpQNormal.IDENTIFIER,
V1HpLogNormal.IDENTIFIER,
V1HpQLogNormal.IDENTIFIER,
}:
raise ValidationError(
"Distribution should not call `to_numpy`, "
"instead it should call `sample`."
)
示例15: logspace
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import geomspace [as 别名]
def logspace(start, stop, num, endpoint=True, base=10.0, unit=None):
"""
See also:
* <nparray.geomspace>
Arguments
------------
* `start` (int or float): ``base ** start`` is the starting value of the sequence.
* `stop` (int or float): ``base ** stop`` is the final value of the sequence,
unless `endpoint` is False. In that case, ``num + 1`` values are spaced
over the interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
* `num` (int): number of samples to generate.
* `endpoint` (bool, optional, default=True): If True, `stop` is the last
sample. Otherwise, it is not included.
* `base` (float, optional, default=10.0): The base of the log space. The
step size between the elements in ``ln(samples) / ln(base)``
(or ``log_base(samples)``) is uniform.
* `unit` (astropy unit or string, optional, default=None): unit
corresponding to the passed values.
Returns
-----------
* <Logspace>
"""
return _wrappers.Logspace(start, stop, num, endpoint, base, unit)