Here are the examples of the python api numpy.logical_not taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : _converter.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
info = self.plot_obj.date_axis_info
if self.isminor:
format = np.compress(info['min'] & np.logical_not(info['maj']),
info)
else:
format = np.compress(info['maj'], info)
self.formatdict = {x: f for (x, _, _, f) in format}
return self.formatdict
3
View Complete Implementation : base.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def drop_duplicates(self, keep='first', inplace=False):
inplace = validate_bool_kwarg(inplace, 'inplace')
if isinstance(self, ABCIndexClast):
if self.is_unique:
return self._shallow_copy()
duplicated = self.duplicated(keep=keep)
result = self[np.logical_not(duplicated)]
if inplace:
return self._update_inplace(result)
else:
return result
3
View Complete Implementation : util.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : WinVector
Copyright BSD 3-Clause "New" or "Revised" License
Author : WinVector
def summarize_column(x, *, fn=numpy.mean):
"""
Summarize column to a non-missing scalar.
:param x: a vector/Series or column of numbers
:param fn: summarize function (such as numpy.mean), only pasted non-bad positions
:return: scalar float summary of the non-None positions of x (otherwise 0)
"""
x = safe_to_numeric_array(x)
not_bad = numpy.logical_not(is_bad(x))
n_not_bad = sum(not_bad)
if n_not_bad < 1:
return 0.0
x = x[not_bad]
v = 0.0 + fn(x)
if pandas.isnull(v) or math.isnan(v) or math.isinf(v):
return 0.0
return v
3
View Complete Implementation : util.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : WinVector
Copyright BSD 3-Clause "New" or "Revised" License
Author : WinVector
def summarize_column(x, *, fn=numpy.mean):
"""
Summarize column to a non-missing scalar.
:param x: a vector/Series or column of numbers
:param fn: summarize function (such as numpy.mean), only pasted non-bad positions
:return: scalar float summary of the non-None positions of x (otherwise 0)
"""
x = safe_to_numeric_array(x)
not_bad = numpy.logical_not(is_bad(x))
n_not_bad = sum(not_bad)
if n_not_bad < 1:
return 0.0
x = x[not_bad]
v = 0.0 + fn(x)
if pandas.isnull(v) or math.isnan(v) or math.isinf(v):
return 0.0
return v
3
View Complete Implementation : ogive.py
Copyright Apache License 2.0
Author : Knewton
Copyright Apache License 2.0
Author : Knewton
@staticmethod
def bernoulli_logli(trues, probs, average=False):
""" Compute the log-likelihood of all the data, given the Bernoulli probabilities.
:param np.ndarray trues: array of boolean data values
:param np.ndarray probs: array of probability of true
:param bool average: whether to return the average log-likelihood, rather than the sum
:return: the log-likelihood
:rtype: float
"""
if trues.shape != probs.shape:
raise ValueError("trues and probs have shapes {} and {}, must be numpy arrays of same "
"shape".format(trues.shape, probs.shape))
falses = np.logical_not(trues)
log_li = np.sum(np.log(probs[trues])) + np.sum(np.log(1.0 - probs[falses]))
if average:
return log_li / trues.size
else:
return log_li
3
View Complete Implementation : metrics.py
Copyright Apache License 2.0
Author : Knewton
Copyright Apache License 2.0
Author : Knewton
@staticmethod
def d_prime_helper(data, prob_true):
""" Compute the d-prime metric (of the separation of probabilities astociated with positive
data labels and negative data labels).
:param np.ndarray[bool] data: binary data values (positive/negative clast labels).
:param np.ndarray[float] prob_true: probability of positive label
:return: d-prime metric
:rtype: float
"""
if len(prob_true) != len(data):
raise ValueError('prob_true and data must have the same length')
prob_true, data = Metrics._check_finite(prob_true, data)
pc_correct = prob_true[data]
pc_incorrect = prob_true[np.logical_not(data)]
mean_sep = np.mean(pc_correct) - np.mean(pc_incorrect)
norm_const = np.sqrt(0.5 * (np.var(pc_correct) + np.var(pc_incorrect)))
return mean_sep / norm_const
3
View Complete Implementation : _converter.py
Copyright MIT License
Author : jgagneastro
Copyright MIT License
Author : jgagneastro
def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
info = self.plot_obj.date_axis_info
if self.isminor:
format = np.compress(info['min'] & np.logical_not(info['maj']),
info)
else:
format = np.compress(info['maj'], info)
self.formatdict = {x: f for (x, _, _, f) in format}
return self.formatdict
3
View Complete Implementation : _converter.py
Copyright Apache License 2.0
Author : Frank-qlu
Copyright Apache License 2.0
Author : Frank-qlu
def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
info = self.plot_obj.date_axis_info
if self.isminor:
format = np.compress(info['min'] & np.logical_not(info['maj']),
info)
else:
format = np.compress(info['maj'], info)
self.formatdict = {x: f for (x, _, _, f) in format}
return self.formatdict
3
View Complete Implementation : _converter.py
Copyright Apache License 2.0
Author : Frank-qlu
Copyright Apache License 2.0
Author : Frank-qlu
def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
if self.plot_obj.date_axis_info is None:
self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq)
info = self.plot_obj.date_axis_info
if self.isminor:
format = np.compress(info['min'] & np.logical_not(info['maj']),
info)
else:
format = np.compress(info['maj'], info)
self.formatdict = {x: f for (x, _, _, f) in format}
return self.formatdict
3
View Complete Implementation : transformed_distribution.py
Copyright MIT License
Author : PacktPublishing
Copyright MIT License
Author : PacktPublishing
def _logical_not(x):
"""Convenience function which attempts to statically apply `logical_not`."""
x_ = _static_value(x)
if x_ is None:
return math_ops.logical_not(x)
return constant_op.constant(np.logical_not(x_))