code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def assignClasses(self):
"""Ensure that the class field is properly defined and nClasses is set.
"""
if len(self['class']) < len(self['target']):
if self.outdim > 1:
raise IndexError('Classes and 1-of-k representation out of sync!')
else:
s... | Ensure that the class field is properly defined and nClasses is set.
| assignClasses | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def getClass(self, idx):
"""Return the label of given class."""
try:
return self.class_labels[idx]
except IndexError:
print("error: classes not defined yet!") | Return the label of given class. | getClass | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def _convertToOneOfMany(self, bounds=(0, 1)):
"""Converts the target classes to a 1-of-k representation, retaining the
old targets as a field `class`.
To supply specific bounds, set the `bounds` parameter, which consists of
target values for non-membership and membership."""
if ... | Converts the target classes to a 1-of-k representation, retaining the
old targets as a field `class`.
To supply specific bounds, set the `bounds` parameter, which consists of
target values for non-membership and membership. | _convertToOneOfMany | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def splitByClass(self, cls_select):
"""Produce two new datasets, the first one comprising only the class
selected (0..nClasses-1), the second one containing the remaining
samples."""
leftIndices, dummy = where(self['class'] == cls_select)
rightIndices, dummy = where(self['class']... | Produce two new datasets, the first one comprising only the class
selected (0..nClasses-1), the second one containing the remaining
samples. | splitByClass | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def castToRegression(self, values):
"""Converts data set into a SupervisedDataSet for regression. Classes
are used as indices into the value array given."""
regDs = SupervisedDataSet(self.indim, 1)
fields = self.getFieldNames()
fields.remove('target')
for f in fields:
... | Converts data set into a SupervisedDataSet for regression. Classes
are used as indices into the value array given. | castToRegression | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def __init__(self, inp, target, nb_classes=0, class_labels=None):
"""Initialize an empty dataset.
`inp` is used to specify the dimensionality of the input. While the
number of targets is given by implicitly by the training samples, it can
also be set explicity by `nb_classes`. To give t... | Initialize an empty dataset.
`inp` is used to specify the dimensionality of the input. While the
number of targets is given by implicitly by the training samples, it can
also be set explicity by `nb_classes`. To give the classes names, supply
an iterable of strings as `class_labels`. | __init__ | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def stratifiedSplit(self, testfrac=0.15, evalfrac=0):
"""Stratified random split of a sequence data set, i.e. (almost) same
proportion of sequences in each class for all fragments. Return
(training, test[, eval]) data sets.
The parameter `testfrac` specifies the fraction of total sequen... | Stratified random split of a sequence data set, i.e. (almost) same
proportion of sequences in each class for all fragments. Return
(training, test[, eval]) data sets.
The parameter `testfrac` specifies the fraction of total sequences in
the test dataset, while `evalfrac` specifies the f... | stratifiedSplit | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def getSequenceClass(self, index=None):
"""Return a flat array (or single scalar) comprising one class per
sequence as given by last pattern in each sequence."""
lastSeq = self.getNumSequences() - 1
if index is None:
classidx = r_[self['sequence_index'].astype(int)[1:, 0] - 1... | Return a flat array (or single scalar) comprising one class per
sequence as given by last pattern in each sequence. | getSequenceClass | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def removeSequence(self, index):
"""Remove sequence (including class field) from the dataset."""
self.assignClasses()
self.linkFields(['input', 'target', 'class'])
SequentialDataSet.removeSequence(self, index)
self.unlinkFields(['class']) | Remove sequence (including class field) from the dataset. | removeSequence | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def save_netcdf(self, flo, **kwargs):
"""Save the current dataset to the given file as a netCDF dataset to be
used with Alex Graves nnl_ndim program in
task="sequence classification" mode."""
# make sure classes are defined properly
assert len(self['class']) == len(self['target']... | Save the current dataset to the given file as a netCDF dataset to be
used with Alex Graves nnl_ndim program in
task="sequence classification" mode. | save_netcdf | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def setVectorFormat(self, vf):
"""Determine which format to use for returning vectors. Use the property vectorformat.
:key type: possible types are '1d', '2d', 'list'
'1d' - example: array([1,2,3])
'2d' - example: array([[1,2,3]])
'list' - example... | Determine which format to use for returning vectors. Use the property vectorformat.
:key type: possible types are '1d', '2d', 'list'
'1d' - example: array([1,2,3])
'2d' - example: array([[1,2,3]])
'list' - example: [1,2,3]
'none' - no conv... | setVectorFormat | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def _convertArray2d(self, vector, column=False):
"""Converts the incoming `vector` to a 2d vector with shape (1,x), or
(x,1) if `column` is set, where x is the number of elements."""
a = asarray(vector)
sh = a.shape
# also reshape scalar values to 2d-index
if len(sh) == 0... | Converts the incoming `vector` to a 2d vector with shape (1,x), or
(x,1) if `column` is set, where x is the number of elements. | _convertArray2d | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def addField(self, label, dim):
"""Add a field to the dataset.
A field consists of a string `label` and a numpy ndarray of dimension
`dim`."""
self.data[label] = zeros((0, dim), float)
self.endmarker[label] = 0 | Add a field to the dataset.
A field consists of a string `label` and a numpy ndarray of dimension
`dim`. | addField | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def setField(self, label, arr):
"""Set the given array `arr` as the new array of field `label`,"""
as_arr = asarray(arr)
self.data[label] = as_arr
self.endmarker[label] = as_arr.shape[0] | Set the given array `arr` as the new array of field `label`, | setField | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def linkFields(self, linklist):
"""Link the length of several fields given by the list of strings
`linklist`."""
length = self[linklist[0]].shape[0]
for l in linklist:
if self[l].shape[0] != length:
raise OutOfSyncError
self.link = linklist | Link the length of several fields given by the list of strings
`linklist`. | linkFields | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def unlinkFields(self, unlinklist=None):
"""Remove fields from the link list or clears link given by the list of
string `linklist`.
This method has no effect if fields are not linked."""
link = self.link
if unlinklist is not None:
for l in unlinklist:
... | Remove fields from the link list or clears link given by the list of
string `linklist`.
This method has no effect if fields are not linked. | unlinkFields | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def getDimension(self, label):
"""Return the dimension/number of columns for the field given by
`label`."""
try:
dim = self.data[label].shape[1]
except KeyError:
raise KeyError('dataset field %s not found.' % label)
return dim | Return the dimension/number of columns for the field given by
`label`. | getDimension | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def getLength(self):
"""Return the length of the linked data fields. If no linked fields exist,
return the length of the longest field."""
if self.link == []:
try:
length = self.endmarker[max(self.endmarker)]
except ValueError:
return 0
... | Return the length of the linked data fields. If no linked fields exist,
return the length of the longest field. | getLength | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def _resizeArray(self, a):
"""Increase the buffer size. It should always be one longer than the
current sequence length and double on every growth step."""
shape = list(a.shape)
shape[0] = (shape[0] + 1) * 2
return resize(a, shape) | Increase the buffer size. It should always be one longer than the
current sequence length and double on every growth step. | _resizeArray | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def _appendUnlinked(self, label, row):
"""Append `row` to the field array with the given `label`.
Do not call this function from outside, use ,append() instead.
Automatically casts vector to a 2d (or higher) shape."""
if self.data[label].shape[0] <= self.endmarker[label]:
se... | Append `row` to the field array with the given `label`.
Do not call this function from outside, use ,append() instead.
Automatically casts vector to a 2d (or higher) shape. | _appendUnlinked | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def append(self, label, row):
"""Append `row` to the array given by `label`.
If the field is linked with others, the function throws an
`OutOfSyncError` because all linked fields always have to have the same
length. If you want to add a row to all linked fields, use appendLink
i... | Append `row` to the array given by `label`.
If the field is linked with others, the function throws an
`OutOfSyncError` because all linked fields always have to have the same
length. If you want to add a row to all linked fields, use appendLink
instead. | append | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def appendLinked(self, *args):
"""Add rows to all linked fields at once."""
assert len(args) == len(self.link)
for i, l in enumerate(self.link):
self._appendUnlinked(l, args[i]) | Add rows to all linked fields at once. | appendLinked | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def getLinked(self, index=None):
"""Access the dataset randomly or sequential.
If called with `index`, the appropriate line consisting of all linked
fields is returned and the internal marker is set to the next line.
Otherwise the marked line is returned and the marker is moved to the
... | Access the dataset randomly or sequential.
If called with `index`, the appropriate line consisting of all linked
fields is returned and the internal marker is set to the next line.
Otherwise the marked line is returned and the marker is moved to the
next line. | getLinked | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def getField(self, label):
"""Return the entire field given by `label` as an array or list,
depending on user settings."""
# Note: label_data should always be a np.array, so this will never
# actually clone a list (performances are O(1)).
label_data = self.data[label][:self.endma... | Return the entire field given by `label` as an array or list,
depending on user settings. | getField | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def convertField(self, label, newtype):
"""Convert the given field to a different data type."""
try:
self.setField(label, self.data[label].astype(newtype))
except KeyError:
raise KeyError('convertField: dataset field %s not found.' % label) | Convert the given field to a different data type. | convertField | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def clear(self, unlinked=False):
"""Clear the dataset.
If linked fields exist, only the linked fields will be deleted unless
`unlinked` is set to True. If no fields are linked, all data will be
deleted."""
self.reset()
keys = self.link
if keys == [] or unlinked:
... | Clear the dataset.
If linked fields exist, only the linked fields will be deleted unless
`unlinked` is set to True. If no fields are linked, all data will be
deleted. | clear | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def reconstruct(cls, filename):
"""Read an incomplete data set (option arraysonly) into the given one. """
# FIXME: Obsolete! Kept here because of some old files...
obj = cls(1, 1)
for key, val in pickle.load(file(filename)).items():
obj.setField(key, val)
return obj | Read an incomplete data set (option arraysonly) into the given one. | reconstruct | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def save_pickle(self, flo, protocol=0, compact=False):
"""Save data set as pickle, removing empty space if desired."""
if compact:
# remove padding of zeros for each field
for field in self.getFieldNames():
temp = self[field][0:self.endmarker[field] + 1, :]
... | Save data set as pickle, removing empty space if desired. | save_pickle | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def batches(self, label, n, permutation=None):
"""Yield batches of the size of n from the dataset.
A single batch is an array of with dim columns and n rows. The last
batch is possibly smaller.
If permutation is given, batches are yielded in the corresponding
order."""
... | Yield batches of the size of n from the dataset.
A single batch is an array of with dim columns and n rows. The last
batch is possibly smaller.
If permutation is given, batches are yielded in the corresponding
order. | batches | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def randomBatches(self, label, n):
"""Like .batches(), but the order is random."""
permutation = random.shuffle(list(range(len(self))))
return self.batches(label, n, permutation) | Like .batches(), but the order is random. | randomBatches | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def replaceNansByMeans(self):
"""Replace all not-a-number entries in the dataset by the means of the
corresponding column."""
for d in self.data.values():
means = scipy.nansum(d[:self.getLength()], axis=0) / self.getLength()
for i in range(self.getLength()):
... | Replace all not-a-number entries in the dataset by the means of the
corresponding column. | replaceNansByMeans | python | pybrain/pybrain | pybrain/datasets/dataset.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/dataset.py | BSD-3-Clause |
def addSample(self, inp, target, importance=None):
""" adds a new sample consisting of input, target and importance.
:arg inp: the input of the sample
:arg target: the target of the sample
:key importance: the importance of the sample. If left None, the
impo... | adds a new sample consisting of input, target and importance.
:arg inp: the input of the sample
:arg target: the target of the sample
:key importance: the importance of the sample. If left None, the
importance will be set to 1.0
| addSample | python | pybrain/pybrain | pybrain/datasets/importance.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/importance.py | BSD-3-Clause |
def _evaluateSequence(self, f, seq, verbose = False):
""" return the importance-ponderated MSE over one sequence. """
totalError = 0
ponderation = 0.
for input, target, importance in seq:
res = f(input)
e = 0.5 * dot(importance.flatten(), ((target-res).flatten()**... | return the importance-ponderated MSE over one sequence. | _evaluateSequence | python | pybrain/pybrain | pybrain/datasets/importance.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/importance.py | BSD-3-Clause |
def __init__(self, statedim, actiondim):
""" initialize the reinforcement dataset, add the 3 fields state, action and
reward, and create an index marker. This class is basically a wrapper function
that renames the fields of SupervisedDataSet into the more common reinforcement
... | initialize the reinforcement dataset, add the 3 fields state, action and
reward, and create an index marker. This class is basically a wrapper function
that renames the fields of SupervisedDataSet into the more common reinforcement
learning names. Instead of 'episodes' though, we de... | __init__ | python | pybrain/pybrain | pybrain/datasets/reinforcement.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/reinforcement.py | BSD-3-Clause |
def newSequence(self):
"""Marks the beginning of a new sequence. this function does nothing if
called at the very start of the data set. Otherwise, it starts a new
sequence. Empty sequences are not allowed, and an EmptySequenceError
exception will be raised."""
length = self.getL... | Marks the beginning of a new sequence. this function does nothing if
called at the very start of the data set. Otherwise, it starts a new
sequence. Empty sequences are not allowed, and an EmptySequenceError
exception will be raised. | newSequence | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def _getSequenceField(self, index, field):
"""Return a sequence of one single field given by `field` and indexed by
`index`."""
seq = ravel(self.getField('sequence_index'))
if len(seq) == index + 1:
# user wants to access the last sequence, return until end of data
... | Return a sequence of one single field given by `field` and indexed by
`index`. | _getSequenceField | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def endOfSequence(self, index):
"""Return True if the marker was moved over the last element of
sequence `index`, False otherwise.
Mostly used like .endOfData() with while loops."""
seq = ravel(self.getField('sequence_index'))
if len(seq) == index + 1:
# user wants t... | Return True if the marker was moved over the last element of
sequence `index`, False otherwise.
Mostly used like .endOfData() with while loops. | endOfSequence | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def gotoSequence(self, index):
"""Move the internal marker to the beginning of sequence `index`."""
try:
self.index = ravel(self.getField('sequence_index'))[index]
except IndexError:
raise IndexError('sequence does not exist') | Move the internal marker to the beginning of sequence `index`. | gotoSequence | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def getCurrentSequence(self):
"""Return the current sequence, according to the marker position."""
seq = ravel(self.getField('sequence_index'))
return len(seq) - sum(seq > self.index) - 1 | Return the current sequence, according to the marker position. | getCurrentSequence | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def getSequenceLength(self, index):
"""Return the length of the given sequence. If `index` is pointing
to the last sequence, the sequence is considered to go until the end
of the dataset."""
seq = ravel(self.getField('sequence_index'))
if len(seq) == index + 1:
# user... | Return the length of the given sequence. If `index` is pointing
to the last sequence, the sequence is considered to go until the end
of the dataset. | getSequenceLength | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def removeSequence(self, index):
"""Remove the `index`'th sequence from the dataset and places the
marker to the sample following the removed sequence."""
if index >= self.getNumSequences():
# sequence doesn't exist, raise exception
raise IndexError('sequence does not exi... | Remove the `index`'th sequence from the dataset and places the
marker to the sample following the removed sequence. | removeSequence | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def evaluateModuleMSE(self, module, averageOver=1, **args):
"""Evaluate the predictions of a module on a sequential dataset
and return the MSE (potentially average over a number of epochs)."""
res = 0.
for dummy in range(averageOver):
ponderation = 0.
totalError =... | Evaluate the predictions of a module on a sequential dataset
and return the MSE (potentially average over a number of epochs). | evaluateModuleMSE | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def splitWithProportion(self, proportion=0.5):
"""Produce two new datasets, each containing a part of the sequences.
The first dataset will have a fraction given by `proportion` of the
dataset."""
l = self.getNumSequences()
leftIndices = sample(list(range(l)), int(l * proportion... | Produce two new datasets, each containing a part of the sequences.
The first dataset will have a fraction given by `proportion` of the
dataset. | splitWithProportion | python | pybrain/pybrain | pybrain/datasets/sequential.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/sequential.py | BSD-3-Clause |
def __init__(self, inp, target):
"""Initialize an empty supervised dataset.
Pass `inp` and `target` to specify the dimensions of the input and
target vectors."""
DataSet.__init__(self)
if isscalar(inp):
# add input and target fields and link them
self.add... | Initialize an empty supervised dataset.
Pass `inp` and `target` to specify the dimensions of the input and
target vectors. | __init__ | python | pybrain/pybrain | pybrain/datasets/supervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/supervised.py | BSD-3-Clause |
def setField(self, label, arr, **kwargs):
"""Set the given array `arr` as the new array of the field specfied by
`label`."""
DataSet.setField(self, label, arr, **kwargs)
# refresh dimensions, in case any of these fields were modified
if label == 'input':
self.indim = ... | Set the given array `arr` as the new array of the field specfied by
`label`. | setField | python | pybrain/pybrain | pybrain/datasets/supervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/supervised.py | BSD-3-Clause |
def evaluateMSE(self, f, **args):
"""Evaluate the predictions of a function on the dataset and return the
Mean Squared Error, incorporating importance."""
ponderation = 0.
totalError = 0
for seq in self._provideSequences():
e, p = self._evaluateSequence(f, seq, **args... | Evaluate the predictions of a function on the dataset and return the
Mean Squared Error, incorporating importance. | evaluateMSE | python | pybrain/pybrain | pybrain/datasets/supervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/supervised.py | BSD-3-Clause |
def _evaluateSequence(self, f, seq, verbose = False):
"""Return the ponderated MSE over one sequence."""
totalError = 0.
ponderation = 0.
for input, target in seq:
res = f(input)
e = 0.5 * sum((target-res).flatten()**2)
totalError += e
pond... | Return the ponderated MSE over one sequence. | _evaluateSequence | python | pybrain/pybrain | pybrain/datasets/supervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/supervised.py | BSD-3-Clause |
def evaluateModuleMSE(self, module, averageOver = 1, **args):
"""Evaluate the predictions of a module on a dataset and return the MSE
(potentially average over a number of epochs)."""
res = 0.
for dummy in range(averageOver):
module.reset()
res += self.evaluateMSE... | Evaluate the predictions of a module on a dataset and return the MSE
(potentially average over a number of epochs). | evaluateModuleMSE | python | pybrain/pybrain | pybrain/datasets/supervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/supervised.py | BSD-3-Clause |
def splitWithProportion(self, proportion = 0.5):
"""Produce two new datasets, the first one containing the fraction given
by `proportion` of the samples."""
indicies = random.permutation(len(self))
separator = int(len(self) * proportion)
leftIndicies = indicies[:separator]
... | Produce two new datasets, the first one containing the fraction given
by `proportion` of the samples. | splitWithProportion | python | pybrain/pybrain | pybrain/datasets/supervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/supervised.py | BSD-3-Clause |
def __init__(self, dim):
"""Initialize an empty unsupervised dataset.
Pass `dim` to specify the dimensionality of the samples."""
super(UnsupervisedDataSet, self).__init__()
self.addField('sample', dim)
self.linkFields(['sample'])
self.dim = dim
# reset the inde... | Initialize an empty unsupervised dataset.
Pass `dim` to specify the dimensionality of the samples. | __init__ | python | pybrain/pybrain | pybrain/datasets/unsupervised.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/unsupervised.py | BSD-3-Clause |
def _learnStep(self):
""" generate a new evaluable by mutation, compare them, and keep the best. """
# re-evaluate the current individual in case the evaluator is noisy
if self.evaluatorIsNoisy:
self.bestEvaluation = self._oneEvaluation(self.bestEvaluable)
# hill-climbing
... | generate a new evaluable by mutation, compare them, and keep the best. | _learnStep | python | pybrain/pybrain | pybrain/optimization/hillclimber.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/hillclimber.py | BSD-3-Clause |
def __init__(self, evaluator = None, initEvaluable = None, **kwargs):
""" The evaluator is any callable object (e.g. a lambda function).
Algorithm parameters can be set here if provided as keyword arguments. """
# set all algorithm-specific parameters in one go:
self.__minimize = None
... | The evaluator is any callable object (e.g. a lambda function).
Algorithm parameters can be set here if provided as keyword arguments. | __init__ | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def _setMinimize(self, flag):
""" Minimization vs. maximization: priority to algorithm requirements,
then evaluator, default = maximize."""
self.__minimize = flag
opp = False
if flag is True:
if self.mustMaximize:
opp = True
self.__min... | Minimization vs. maximization: priority to algorithm requirements,
then evaluator, default = maximize. | _setMinimize | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def setEvaluator(self, evaluator, initEvaluable = None):
""" If not provided upon construction, the objective function can be given through this method.
If necessary, also provide an initial evaluable."""
# default settings, if provided by the evaluator:
if isinstance(evaluator,... | If not provided upon construction, the objective function can be given through this method.
If necessary, also provide an initial evaluable. | setEvaluator | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def learn(self, additionalLearningSteps = None):
""" The main loop that does the learning. """
assert self.__evaluator is not None, "No evaluator has been set. Learning cannot start."
if additionalLearningSteps is not None:
self.maxLearningSteps = self.numLearningSteps + additionalLe... | The main loop that does the learning. | learn | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def _bestFound(self):
""" return the best found evaluable and its associated fitness. """
bestE = self.bestEvaluable.params.copy() if self._wasWrapped else self.bestEvaluable
if self._wasOpposed and isscalar(self.bestEvaluation):
bestF = -self.bestEvaluation
else:
... | return the best found evaluable and its associated fitness. | _bestFound | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def _oneEvaluation(self, evaluable):
""" This method should be called by all optimizers for producing an evaluation. """
if self._wasUnwrapped:
self.wrappingEvaluable._setParameters(evaluable)
res = self.__evaluator(self.wrappingEvaluable)
elif self._wasWrapped: ... | This method should be called by all optimizers for producing an evaluation. | _oneEvaluation | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def _notify(self):
""" Provide some feedback during the run. """
if self.verbose:
print(('Step:', self.numLearningSteps, 'best:', self.bestEvaluation))
if self.listener is not None:
self.listener(self.bestEvaluable, self.bestEvaluation) | Provide some feedback during the run. | _notify | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def _setInitEvaluable(self, evaluable):
""" If the parameters are wrapped, we keep track of the wrapper explicitly. """
if isinstance(evaluable, ParameterContainer):
self.wrappingEvaluable = evaluable.copy()
self._wasUnwrapped = True
elif not (evaluable is None
... | If the parameters are wrapped, we keep track of the wrapper explicitly. | _setInitEvaluable | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def _bestFound(self):
""" return the best found evaluable and its associated fitness. """
bestE, bestF = BlackBoxOptimizer._bestFound(self)
if self._wasUnwrapped:
self.wrappingEvaluable._setParameters(bestE)
bestE = self.wrappingEvaluable.copy()
return bestE, best... | return the best found evaluable and its associated fitness. | _bestFound | python | pybrain/pybrain | pybrain/optimization/optimizer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/optimizer.py | BSD-3-Clause |
def sorti(vect):
""" sort, but also return the indices-changes """
tmp = sorted([(x_y[1], x_y[0]) for x_y in enumerate(ravel(vect))])
res1 = array([x[0] for x in tmp])
res2 = array([int(x[1]) for x in tmp])
return res1, res2 | sort, but also return the indices-changes | sorti | python | pybrain/pybrain | pybrain/optimization/distributionbased/cmaes.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/cmaes.py | BSD-3-Clause |
def _generateConformingBatch(self):
""" Generate a batch of samples that conforms to the current distribution.
If importance mixing is enabled, this can reuse old samples. """ | Generate a batch of samples that conforms to the current distribution.
If importance mixing is enabled, this can reuse old samples. | _generateConformingBatch | python | pybrain/pybrain | pybrain/optimization/distributionbased/distributionbased.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/distributionbased.py | BSD-3-Clause |
def _produceNewSample(self):
""" returns a new sample, its fitness and its densities """
chosenOne = drawIndex(self.alphas, True)
mu = self.mus[chosenOne]
if self.useAnticipatedMeanShift:
if len(self.allsamples) % 2 == 1 and len(self.allsamples) > 1:
if not(s... | returns a new sample, its fitness and its densities | _produceNewSample | python | pybrain/pybrain | pybrain/optimization/distributionbased/fem.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/fem.py | BSD-3-Clause |
def _computeUpdateSize(self, densities, sampleIndex):
""" compute the the center-update-size for each sample
using transformed fitnesses """
# determine (transformed) fitnesses
transformedfitnesses = self.shapingFunction(self.fitnesses)
# force renormaliziation
transfor... | compute the the center-update-size for each sample
using transformed fitnesses | _computeUpdateSize | python | pybrain/pybrain | pybrain/optimization/distributionbased/fem.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/fem.py | BSD-3-Clause |
def _updateShaping(self):
""" Daan: "This won't work. I like it!" """
assert self.numberOfCenters == 1
possible = self.shapingFunction.getPossibleParameters(self.windowSize)
matchValues = []
pdfs = [multivariateNormalPdf(s, self.mus[0], self.sigmas[0])
for s in s... | Daan: "This won't work. I like it!" | _updateShaping | python | pybrain/pybrain | pybrain/optimization/distributionbased/fem.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/fem.py | BSD-3-Clause |
def _logDerivsFactorSigma(self, samples, mu, invSigma, factorSigma):
""" Compute the log-derivatives w.r.t. the factorized covariance matrix components.
This implementation should be faster than the one in Vanilla. """
res = zeros((len(samples), self.numDistrParams - self.numParameters))
... | Compute the log-derivatives w.r.t. the factorized covariance matrix components.
This implementation should be faster than the one in Vanilla. | _logDerivsFactorSigma | python | pybrain/pybrain | pybrain/optimization/distributionbased/nes.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/nes.py | BSD-3-Clause |
def _produceSamples(self):
""" Append batch size new samples and evaluate them. """
tmp = [self._sample2base(self._produceSample()) for _ in range(self.batchSize)]
list(map(self._oneEvaluation, tmp))
self._pointers = list(range(len(self._allEvaluated) - self.batchSize, len(se... | Append batch size new samples and evaluate them. | _produceSamples | python | pybrain/pybrain | pybrain/optimization/distributionbased/rank1.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/rank1.py | BSD-3-Clause |
def _notify(self):
""" Provide some feedback during the run. """
if self.verbose:
if self.numEvaluations % self.verboseGaps == 0:
print(('Step:', self.numLearningSteps, 'best:', self.bestEvaluation,
'logVar', round(self._logDetA, 3),
'l... | Provide some feedback during the run. | _notify | python | pybrain/pybrain | pybrain/optimization/distributionbased/rank1.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/rank1.py | BSD-3-Clause |
def test():
""" Rank-1 NEX easily solves high-dimensional Rosenbrock functions. """
from pybrain.rl.environments.functions.unimodal import RosenbrockFunction
dim = 40
f = RosenbrockFunction(dim)
x0 = -ones(dim)
l = Rank1NES(f, x0, verbose=True, verboseGaps=500)
l.learn() | Rank-1 NEX easily solves high-dimensional Rosenbrock functions. | test | python | pybrain/pybrain | pybrain/optimization/distributionbased/rank1.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/rank1.py | BSD-3-Clause |
def _produceSamples(self):
""" Append batch size new samples and evaluate them. """
if self.clearStorage:
self._allEvaluated = []
self._allEvaluations = []
tmp = [self._sample2base(self._produceSample()) for _ in range(self.batchSize)]
list(map(self._... | Append batch size new samples and evaluate them. | _produceSamples | python | pybrain/pybrain | pybrain/optimization/distributionbased/snes.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/snes.py | BSD-3-Clause |
def _produceSamples(self):
""" Append batchsize new samples and evaluate them. """
if self.numLearningSteps == 0 or not self.importanceMixing:
for _ in range(self.batchSize):
self._produceNewSample()
self.allGenerated.append(self.batchSize + self.allGenerated[-1])... | Append batchsize new samples and evaluate them. | _produceSamples | python | pybrain/pybrain | pybrain/optimization/distributionbased/ves.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/ves.py | BSD-3-Clause |
def _hasConverged(self):
""" When the largest eigenvalue is smaller than 10e-20, we assume the
algorithms has converged. """
eigs = abs(diag(self.factorSigma))
return min(eigs) < 1e-10 | When the largest eigenvalue is smaller than 10e-20, we assume the
algorithms has converged. | _hasConverged | python | pybrain/pybrain | pybrain/optimization/distributionbased/ves.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/ves.py | BSD-3-Clause |
def _revertToSafety(self):
""" When encountering a bad matrix, this is how we revert to a safe one. """
self.factorSigma = eye(self.numParameters)
self.x = self.bestEvaluable
self.allFactorSigmas[-1][:] = self.factorSigma
self.sigma = dot(self.factorSigma.T, self.factorSigma) | When encountering a bad matrix, this is how we revert to a safe one. | _revertToSafety | python | pybrain/pybrain | pybrain/optimization/distributionbased/ves.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/ves.py | BSD-3-Clause |
def _produceSamples(self):
""" Append batch size new samples and evaluate them. """
reuseindices = []
if self.numLearningSteps == 0 or not self.importanceMixing:
[self._oneEvaluation(self._sample2base(self._produceSample())) for _ in range(self.batchSize)]
self._pointers ... | Append batch size new samples and evaluate them. | _produceSamples | python | pybrain/pybrain | pybrain/optimization/distributionbased/xnes.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/xnes.py | BSD-3-Clause |
def _learnStep(self):
""" calls the gradient calculation function and executes a step in direction
of the gradient, scaled with a small learning rate alpha. """
# initialize matrix D and vector R
D = ones((self.batchSize, self.numParameters))
R = zeros((self.batchSize, 1))
... | calls the gradient calculation function and executes a step in direction
of the gradient, scaled with a small learning rate alpha. | _learnStep | python | pybrain/pybrain | pybrain/optimization/finitedifference/fd.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/finitedifference/fd.py | BSD-3-Clause |
def _learnStep(self):
""" calculates the gradient and executes a step in the direction
of the gradient, scaled with a learning rate alpha. """
deltas = self.perturbation()
#reward of positive and negative perturbations
reward1 = self._oneEvaluation(self.current + deltas) ... | calculates the gradient and executes a step in the direction
of the gradient, scaled with a learning rate alpha. | _learnStep | python | pybrain/pybrain | pybrain/optimization/finitedifference/pgpe.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/finitedifference/pgpe.py | BSD-3-Clause |
def _learnStep(self):
""" calculates the gradient and executes a step in the direction
of the gradient, scaled with a learning rate alpha. """
deltas = self.perturbation()
#reward of positive and negative perturbations
reward1 = self._oneEvaluation(self.current + deltas)
... | calculates the gradient and executes a step in the direction
of the gradient, scaled with a learning rate alpha. | _learnStep | python | pybrain/pybrain | pybrain/optimization/finitedifference/spsa.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/finitedifference/spsa.py | BSD-3-Clause |
def switchMutations(self):
""" interchange the mutate() and topologyMutate() operators """
tm = self._initEvaluable.__class__.topologyMutate
m = self._initEvaluable.__class__.mutate
self._initEvaluable.__class__.topologyMutate = m
self._initEvaluable.__class__.mutate = tm | interchange the mutate() and topologyMutate() operators | switchMutations | python | pybrain/pybrain | pybrain/optimization/memetic/memetic.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/memetic/memetic.py | BSD-3-Clause |
def crossOverOld(self, parents, nbChildren):
""" generate a number of children by doing 1-point cross-over """
xdim = self.numParameters
children = []
for _ in range(nbChildren):
p1 = choice(parents)
if xdim < 2:
children.append(p1)
els... | generate a number of children by doing 1-point cross-over | crossOverOld | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def mutatedOld(self, indiv):
""" mutate some genes of the given individual """
res = indiv.copy()
for i in range(self.numParameters):
if random() < self.mutationProb:
res[i] = indiv[i] + gauss(0, self.mutationStdDev)
return res | mutate some genes of the given individual | mutatedOld | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def crossOver(self, parents, nbChildren):
""" generate a number of children by doing 1-point cross-over """
""" change as the <choice> return quite often the same p1 and even
several time p2 was return the same than p1 """
xdim = self.numParameters
shuffle(parents)
ch... | generate a number of children by doing 1-point cross-over | crossOver | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def mutated(self, indiv):
""" mutate some genes of the given individual """
res = indiv.copy()
#to avoid having a child identical to one of the currentpopulation'''
for i in range(self.numParameters):
if random() < self.mutationProb:
if self.xBound is None:
... | mutate some genes of the given individual | mutated | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def old_jpq_mutated(self, indiv, pop):
""" mutate some genes of the given individual """
res = indiv.copy()
#to avoid having a child identical to one of the currentpopulation'''
in_pop = self.childexist(indiv,pop)
for i in range(self.numParameters):
if random() < self... | mutate some genes of the given individual | old_jpq_mutated | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def select(self):
""" select some of the individuals of the population, taking into account their fitnesses
:return: list of selected parents """
if not self.tournament:
tmp = list(zip(self.fitnesses, self.currentpop))
tmp.sort(key = lambda x: x[0])
tmp2 = li... | select some of the individuals of the population, taking into account their fitnesses
:return: list of selected parents | select | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def produceOffspring(self):
""" produce offspring by selection, mutation and crossover. """
parents = self.select()
es = min(self.eliteSize, self.selectionSize)
self.currentpop = parents[:es]
'''Modified by JPQ '''
nbchildren = self.populationSize - es
if self.pop... | produce offspring by selection, mutation and crossover. | produceOffspring | python | pybrain/pybrain | pybrain/optimization/populationbased/ga.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/ga.py | BSD-3-Clause |
def best(self, particlelist):
"""Return the particle with the best fitness from a list of particles.
"""
picker = min if self.minimize else max
return picker(particlelist, key=lambda p: p.fitness) | Return the particle with the best fitness from a list of particles.
| best | python | pybrain/pybrain | pybrain/optimization/populationbased/pso.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/pso.py | BSD-3-Clause |
def __init__(self, start, minimize):
"""Initialize a Particle at the given start vector."""
self.minimize = minimize
self.dim = scipy.size(start)
self.position = start
self.velocity = scipy.zeros(scipy.size(start))
self.bestPosition = scipy.zeros(scipy.size(start))
... | Initialize a Particle at the given start vector. | __init__ | python | pybrain/pybrain | pybrain/optimization/populationbased/pso.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/pso.py | BSD-3-Clause |
def __init__(self, relEvaluator, seeds, **args):
"""
:arg relevaluator: an anti-symmetric function that can evaluate 2 elements
:arg seeds: a list of initial guesses
"""
# set parameters
self.setArgs(**args)
self.relEvaluator = relEvaluator
if self.tournam... |
:arg relevaluator: an anti-symmetric function that can evaluate 2 elements
:arg seeds: a list of initial guesses
| __init__ | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def learn(self, maxSteps=None):
""" Toplevel function, can be called iteratively.
:return: best evaluable found in the last generation. """
if maxSteps != None:
maxSteps += self.steps
while True:
if maxSteps != None and self.steps + self._stepsPerGeneration() > m... | Toplevel function, can be called iteratively.
:return: best evaluable found in the last generation. | learn | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _extendPopulation(self, seeds, size):
""" build a population, with mutated copies from the provided
seed pool until it has the desired size. """
res = seeds[:]
for dummy in range(size - len(seeds)):
chosen = choice(seeds)
tmp = chosen.copy()
tmp.mu... | build a population, with mutated copies from the provided
seed pool until it has the desired size. | _extendPopulation | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _selectAndReproduce(self, pop, fits):
""" apply selection and reproduction to host population, according to their fitness."""
# combine population with their fitness, then sort, only by fitness
s = list(zip(fits, pop))
shuffle(s)
s.sort(key=lambda x:-x[0])
# select...... | apply selection and reproduction to host population, according to their fitness. | _selectAndReproduce | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _beats(self, h, p):
""" determine the empirically observed score of p playing opp (starting or not).
If they never played, assume 0. """
if (h, p) not in self.allResults:
return 0
else:
hpgames, hscore = self.allResults[(h, p)][1:3]
phgames, pscore... | determine the empirically observed score of p playing opp (starting or not).
If they never played, assume 0. | _beats | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _doTournament(self, pop1, pop2, tournamentSize=None):
""" Play a tournament.
:key tournamentSize: If unspecified, play all-against-all
"""
# TODO: Preferably select high-performing opponents?
for p in pop1:
pop3 = pop2[:]
while p in pop3:
... | Play a tournament.
:key tournamentSize: If unspecified, play all-against-all
| _doTournament | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _globalScore(self, p):
""" The average score over all evaluations for a player. """
if p not in self.allOpponents:
return 0.
scoresum, played = 0., 0
for opp in self.allOpponents[p]:
scoresum += self.allResults[(p, opp)][2]
played += self.allResult... | The average score over all evaluations for a player. | _globalScore | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _sharedSampling(self, numSelect, selectFrom, relativeTo):
""" Build a shared sampling set of opponents """
if numSelect < 1:
return []
# determine the player of selectFrom with the most wins against players from relativeTo (and which ones)
tmp = {}
for p in select... | Build a shared sampling set of opponents | _sharedSampling | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _relEval(self, p, opp):
""" a single relative evaluation (in one direction) with the involved bookkeeping."""
if p not in self.allOpponents:
self.allOpponents[p] = []
self.allOpponents[p].append(opp)
if (p, opp) not in self.allResults:
self.allResults[(p, opp)... | a single relative evaluation (in one direction) with the involved bookkeeping. | _relEval | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/coevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/coevolution.py | BSD-3-Clause |
def _competitiveSharedFitness(self, hosts, parasites):
""" determine the competitive shared fitness for the population of hosts, w.r. to
the population of parasites. """
if len(parasites) == 0:
return [0] * len(hosts)
# determine beat-sum for parasites (nb of games lost)
... | determine the competitive shared fitness for the population of hosts, w.r. to
the population of parasites. | _competitiveSharedFitness | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/competitivecoevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/competitivecoevolution.py | BSD-3-Clause |
def _initPopulation(self, seeds):
""" one part of the seeds for each population, if there's not enough: randomize. """
for s in seeds:
s.parent = None
while len(seeds) < self.numPops:
tmp = choice(seeds).copy()
tmp.randomize()
seeds.append(tmp)
... | one part of the seeds for each population, if there's not enough: randomize. | _initPopulation | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/multipopulationcoevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/multipopulationcoevolution.py | BSD-3-Clause |
def _evaluatePopulation(self):
"""Each individual in main pop plays against
tournSize others of each other population (the best part of them). """
for other in self.pops:
if other == self.pop:
continue
# TODO: parametrize
bestPart = len(other)/... | Each individual in main pop plays against
tournSize others of each other population (the best part of them). | _evaluatePopulation | python | pybrain/pybrain | pybrain/optimization/populationbased/coevolution/multipopulationcoevolution.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/coevolution/multipopulationcoevolution.py | BSD-3-Clause |
def nsga2select(population, fitnesses, survivors, allowequality = True):
"""The NSGA-II selection strategy (Deb et al., 2002).
The number of individuals that survive is given by the survivors parameter."""
fronts = const_non_dominated_sort(population,
key=lambda x: fitnesses[... | The NSGA-II selection strategy (Deb et al., 2002).
The number of individuals that survive is given by the survivors parameter. | nsga2select | python | pybrain/pybrain | pybrain/optimization/populationbased/multiobjective/constnsga2.py | https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/populationbased/multiobjective/constnsga2.py | BSD-3-Clause |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.