code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def apply(self, population):
""" First determines the number of individuals to be created.
Then clones the fittest individuals (=parents), mutates these clones
and adds them to the population.
"""
max_n = population.getMaxNIndividuals()
n = population.getIndividua... | First determines the number of individuals to be created.
Then clones the fittest individuals (=parents), mutates these clones
and adds them to the population.
| apply | python | pybrain/pybrain | pybrain/supervised/evolino/filter.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/filter.py | BSD-3-Clause |
def __init__(self):
""" :key kwargs: See setArgs() method documentation
"""
SimpleGenomeManipulation.__init__(self)
self.mutationVariate = GaussianVariate()
self.mutationVariate.alpha = 0.1
self.verbosity = 0 | :key kwargs: See setArgs() method documentation
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/gfilter.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/gfilter.py | BSD-3-Clause |
def _manipulateValue(self, value):
""" Implementation of the abstract method of class SimpleGenomeManipulation
Set's the x0 value of the variate to value and takes a new sample
value and returns it.
"""
self.mutationVariate.x0 = value
newval = self.mutationVariate... | Implementation of the abstract method of class SimpleGenomeManipulation
Set's the x0 value of the variate to value and takes a new sample
value and returns it.
| _manipulateValue | python | pybrain/pybrain | pybrain/supervised/evolino/gfilter.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/gfilter.py | BSD-3-Clause |
def getSortedIndividualList(self):
""" Returns a sorted list of all individuals with descending fitness values. """
fitness = self._fitness
return sorted(iter(fitness.keys()), key=lambda k:-fitness[k]) | Returns a sorted list of all individuals with descending fitness values. | getSortedIndividualList | python | pybrain/pybrain | pybrain/supervised/evolino/gpopulation.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/gpopulation.py | BSD-3-Clause |
def getGenome(self):
""" Returns the genome created by concatenating the chromosomes supplied
by the sub-individuals.
"""
genome = []
for sub_individual in self._sub_individuals:
genome.append(deepcopy(sub_individual.getGenome()))
return genome | Returns the genome created by concatenating the chromosomes supplied
by the sub-individuals.
| getGenome | python | pybrain/pybrain | pybrain/supervised/evolino/individual.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/individual.py | BSD-3-Clause |
def __init__(self, genome):
""" :key genome: Any kind of nested iteratable container containing
floats as leafs
"""
self.setGenome(genome)
self.id = EvolinoSubIndividual._next_id
EvolinoSubIndividual._next_id += 1 | :key genome: Any kind of nested iteratable container containing
floats as leafs
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/individual.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/individual.py | BSD-3-Clause |
def _validateGenomeLayer(self, layer):
""" Validates the type and state of a layer
"""
assert isinstance(layer, LSTMLayer)
assert not layer.peepholes | Validates the type and state of a layer
| _validateGenomeLayer | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def _getGenomeOfLayer(self, layer):
""" Returns the genome of a single layer.
"""
self._validateGenomeLayer(layer)
dim = layer.outdim
layer_weights = []
connections = self._getInputConnectionsOfLayer(layer)
for cell_idx in range(dim):
# todo: the ev... | Returns the genome of a single layer.
| _getGenomeOfLayer | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def _setGenomeOfLayer(self, layer, weights):
""" Sets the genome of a single layer.
"""
self._validateGenomeLayer(layer)
dim = layer.outdim
connections = self._getInputConnectionsOfLayer(layer)
for cell_idx in range(dim):
cell_weights = weights.pop(0)
... | Sets the genome of a single layer.
| _setGenomeOfLayer | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def setOutputWeightMatrix(self, W):
""" Sets the weight matrix of the output layer's input connection.
"""
c = self._hid_to_out_connection
c.params[:] = W.flatten() | Sets the weight matrix of the output layer's input connection.
| setOutputWeightMatrix | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def _getInputConnectionsOfLayer(self, layer):
""" Returns a list of all input connections for the layer. """
connections = []
for c in sum(list(self._network.connections.values()), []):
if c.outmod is layer:
if not isinstance(c, FullConnection):
ra... | Returns a list of all input connections for the layer. | _getInputConnectionsOfLayer | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def __init__(self, network):
""" :key network: The network to be wrapped
"""
self.network = network
self._output_connection = None
self._last_hidden_layer = None
self._first_hidden_layer = None
self._establishRecurrence() | :key network: The network to be wrapped
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def _establishRecurrence(self):
""" Adds a recurrent full connection from the output layer to the first
hidden layer.
"""
network = self.network
outlayer = self.getOutputLayer()
hid1layer = self.getFirstHiddenLayer()
network.addRecurrentConnection(FullConnecti... | Adds a recurrent full connection from the output layer to the first
hidden layer.
| _establishRecurrence | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def getGenome(self):
""" Returns the Genome of the network.
See class description for more details.
"""
weights = []
for layer in self.getHiddenLayers():
if isinstance(layer, LSTMLayer):
# if layer is not self._recurrence_layer:
wei... | Returns the Genome of the network.
See class description for more details.
| getGenome | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def setGenome(self, weights):
""" Sets the Genome of the network.
See class description for more details.
"""
weights = deepcopy(weights)
for layer in self.getHiddenLayers():
if isinstance(layer, LSTMLayer):
# if layer is not self._recurrence_layer:
... | Sets the Genome of the network.
See class description for more details.
| setGenome | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def injectBackproject(self, injection):
""" Injects a vector into the recurrent connection.
This will be used in the evolino trainingsphase, where the target
values need to be backprojected instead of the real output of the net.
:key injection: vector of length self.network.... | Injects a vector into the recurrent connection.
This will be used in the evolino trainingsphase, where the target
values need to be backprojected instead of the real output of the net.
:key injection: vector of length self.network.outdim
| injectBackproject | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def getOutputConnection(self):
""" Returns the input connection of the output layer. """
if self._output_connection is None:
outlayer = self.getOutputLayer()
lastlayer = self.getLastHiddenLayer()
for c in self.getConnections():
if c.outmod is outlayer:... | Returns the input connection of the output layer. | getOutputConnection | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def getHiddenLayers(self):
""" Returns a list of all hidden layers. """
layers = []
network = self.network
for m in network.modules:
if m not in network.inmodules and m not in network.outmodules:
layers.append(m)
return layers | Returns a list of all hidden layers. | getHiddenLayers | python | pybrain/pybrain | pybrain/supervised/evolino/networkwrapper.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/networkwrapper.py | BSD-3-Clause |
def __init__(self, individual, subPopulationSize, nCombinations=1, valueInitializer=Randomization(-0.1, 0.1), **kwargs):
""" :key individual: A prototype individual which is used to determine
the structure of the genome.
:key subPopulationSize: integer describing the s... | :key individual: A prototype individual which is used to determine
the structure of the genome.
:key subPopulationSize: integer describing the size of the subpopulations
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/population.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/population.py | BSD-3-Clause |
def getIndividuals(self):
""" Returns a set of individuals of type EvolinoIndividual. The individuals
are generated on the fly. Note that each subpopulation has the same size.
So the number of resulting EvolinoIndividuals is subPopulationSize,
since each chromosome of each su... | Returns a set of individuals of type EvolinoIndividual. The individuals
are generated on the fly. Note that each subpopulation has the same size.
So the number of resulting EvolinoIndividuals is subPopulationSize,
since each chromosome of each subpopulation will be assembled once.
... | getIndividuals | python | pybrain/pybrain | pybrain/supervised/evolino/population.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/population.py | BSD-3-Clause |
def setIndividualFitness(self, individual, fitness):
""" The fitness value is not stored directly inside this population,
but is propagated to the subpopulations of all the subindividuals
of which the individual consists of.
The individual's fitness value is only adjusted if ... | The fitness value is not stored directly inside this population,
but is propagated to the subpopulations of all the subindividuals
of which the individual consists of.
The individual's fitness value is only adjusted if its bigger than
the old value.
To reset ... | setIndividualFitness | python | pybrain/pybrain | pybrain/supervised/evolino/population.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/population.py | BSD-3-Clause |
def __init__(self, chromosome, maxNIndividuals, valueInitializer=Randomization(-0.1, 0.1), **kwargs):
""" :key chromosome: The prototype chromosome
:key maxNIndividuals: The maximum allowed number of individuals
"""
SimplePopulation.__init__(self)
self._prototype = EvolinoSu... | :key chromosome: The prototype chromosome
:key maxNIndividuals: The maximum allowed number of individuals
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/population.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/population.py | BSD-3-Clause |
def __init__(self, min_val=0., max_val=1.):
""" Initializes the uniform variate with a min and a max value.
"""
self._min_val = min_val
self._max_val = max_val | Initializes the uniform variate with a min and a max value.
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/variate.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/variate.py | BSD-3-Clause |
def __init__(self, x0=0., alpha=1.):
""" :key x0: Median and mode of the Cauchy distribution
:key alpha: scale
"""
self.x0 = x0
self.alpha = alpha | :key x0: Median and mode of the Cauchy distribution
:key alpha: scale
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/variate.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/variate.py | BSD-3-Clause |
def __init__(self, x0=0., alpha=1.):
""" :key x0: Mean
:key alpha: standard deviation
"""
self.x0 = x0
self.alpha = alpha | :key x0: Mean
:key alpha: standard deviation
| __init__ | python | pybrain/pybrain | pybrain/supervised/evolino/variate.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/evolino/variate.py | BSD-3-Clause |
def arrayPermutation(permutation):
"""Return a permutation function.
The function permutes any array as specified by the supplied permutation.
"""
assert permutation.ndim == 1, \
"Only one dimensional permutaton arrays are supported"
def permute(arr):
assert arr.ndim == 1, "Only... | Return a permutation function.
The function permutes any array as specified by the supplied permutation.
| arrayPermutation | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def jacardCoefficient(a, b):
"""Return the Jacard coefficient of a and b.
The jacard coefficient is defined as the overlap between two sets: the sum
of all equal elements divided by the size of the sets.
Mind that a and b must b in Hamming space, so every element must either be
1 or 0.
"""
... | Return the Jacard coefficient of a and b.
The jacard coefficient is defined as the overlap between two sets: the sum
of all equal elements divided by the size of the sets.
Mind that a and b must b in Hamming space, so every element must either be
1 or 0.
| jacardCoefficient | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def __init__(self, dim, nPermutations):
"""Create a hash structure that can hold arrays of size dim and
hashes with nPermutations permutations.
The number of buckets is dim * nPermutations."""
self.dim = dim
self.permutations = array([permutation(dim)
... | Create a hash structure that can hold arrays of size dim and
hashes with nPermutations permutations.
The number of buckets is dim * nPermutations. | __init__ | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def _firstOne(self, arr):
"""Return the index of the first 1 in the array."""
for i, elem in enumerate(arr):
if elem == 1:
return i
return i + 1 | Return the index of the first 1 in the array. | _firstOne | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def _hash(self, item):
"""Return a hash for item based on the internal permutations.
That hash is a tuple of ints.
"""
self._checkItem(item)
result = []
for perm in self._permFuncs:
permuted = perm(item)
result.append(self._firstOne(permuted))
... | Return a hash for item based on the internal permutations.
That hash is a tuple of ints.
| _hash | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def put(self, item, satellite):
"""Put an item into the hash structure and attach any object satellite
to it."""
self._checkItem(item)
item = item.astype(bool)
bucket = self._hash(item)
self.buckets[bucket].append((item, satellite)) | Put an item into the hash structure and attach any object satellite
to it. | put | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def knn(self, item, k):
"""Return the k nearest neighbours of the item in the current hash.
Mind that the probabilistic nature of the data structure might not
return a nearest neighbor at all.
"""
self._checkItem(item)
candidates = self.buckets[self._hash(item)]
... | Return the k nearest neighbours of the item in the current hash.
Mind that the probabilistic nature of the data structure might not
return a nearest neighbor at all.
| knn | python | pybrain/pybrain | pybrain/supervised/knn/lsh/minhash.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/minhash.py | BSD-3-Clause |
def __init__(self, dim, omega=4, prob=0.8):
"""Create a hash for arrays of dimension dim.
The hyperspace will be split into hypercubes with a sidelength of
omega * sqrt(sqrt(dim)), that is omega * radius.
Every point in the dim-dimensional euclidean space will be hashed to
its ... | Create a hash for arrays of dimension dim.
The hyperspace will be split into hypercubes with a sidelength of
omega * sqrt(sqrt(dim)), that is omega * radius.
Every point in the dim-dimensional euclidean space will be hashed to
its correct bucket with a probability of prob.
| __init__ | python | pybrain/pybrain | pybrain/supervised/knn/lsh/nearoptimal.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/nearoptimal.py | BSD-3-Clause |
def _findHypercube(self, point):
"""Return where a point lies in what hypercube.
The result is a pair of two arrays. The first array is an array of
integers that indicate the multidimensional index of the hypercube it
is in. The second array is an array of floats, specifying the
... | Return where a point lies in what hypercube.
The result is a pair of two arrays. The first array is an array of
integers that indicate the multidimensional index of the hypercube it
is in. The second array is an array of floats, specifying the
coordinates of the point in that hypercube.... | _findHypercube | python | pybrain/pybrain | pybrain/supervised/knn/lsh/nearoptimal.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/nearoptimal.py | BSD-3-Clause |
def _findLocalBall_noinline(self, point):
"""Return the index of the ball that the point lies in."""
for i, ball in enumerate(self.gridBalls):
distance = point - ball
if dot(distance.T, distance) <= self.radiusSquared:
return i | Return the index of the ball that the point lies in. | _findLocalBall_noinline | python | pybrain/pybrain | pybrain/supervised/knn/lsh/nearoptimal.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/nearoptimal.py | BSD-3-Clause |
def insert(self, point, satellite):
"""Put a point and its satellite information into the hash structure.
"""
point = dot(self.projection, point)
index = self.findBall(point)
self.balls[index].append((point, satellite)) | Put a point and its satellite information into the hash structure.
| insert | python | pybrain/pybrain | pybrain/supervised/knn/lsh/nearoptimal.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/nearoptimal.py | BSD-3-Clause |
def _findKnnCandidates(self, point):
"""Return a set of candidates that might be nearest neighbours of a
query point."""
index = self.findBall(point)
logging.debug("Found %i candidates for cNN" % len(self.balls[index]))
return self.balls[index] | Return a set of candidates that might be nearest neighbours of a
query point. | _findKnnCandidates | python | pybrain/pybrain | pybrain/supervised/knn/lsh/nearoptimal.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/nearoptimal.py | BSD-3-Clause |
def knn(self, point, k):
"""Return the k approximate nearest neighbours of the item in the
current hash.
Mind that the probabilistic nature of the data structure might not
return a nearest neighbor at all and not the nearest neighbour."""
candidates = self._findKnnCandidates(po... | Return the k approximate nearest neighbours of the item in the
current hash.
Mind that the probabilistic nature of the data structure might not
return a nearest neighbor at all and not the nearest neighbour. | knn | python | pybrain/pybrain | pybrain/supervised/knn/lsh/nearoptimal.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/knn/lsh/nearoptimal.py | BSD-3-Clause |
def __init__(self, module, dataset=None, learningrate=0.01, lrdecay=1.0,
momentum=0., verbose=False, batchlearning=False,
weightdecay=0.):
"""Create a BackpropTrainer to train the specified `module` on the
specified `dataset`.
The learning rate gives the ratio ... | Create a BackpropTrainer to train the specified `module` on the
specified `dataset`.
The learning rate gives the ratio of which parameters are changed into
the direction of the gradient. The learning rate decreases by `lrdecay`,
which is used to to multiply the learning rate after each ... | __init__ | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def train(self):
"""Train the associated module for one epoch."""
assert len(self.ds) > 0, "Dataset cannot be empty."
self.module.resetDerivatives()
errors = 0
ponderation = 0.
shuffledSequences = []
for seq in self.ds._provideSequences():
shuffledSequ... | Train the associated module for one epoch. | train | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def _calcDerivs(self, seq):
"""Calculate error function and backpropagate output errors to yield
the gradient."""
self.module.reset()
for sample in seq:
self.module.activate(sample[0])
error = 0
ponderation = 0.
for offset, sample in reversed(list(enum... | Calculate error function and backpropagate output errors to yield
the gradient. | _calcDerivs | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def _checkGradient(self, dataset=None, silent=False):
"""Numeric check of the computed gradient for debugging purposes."""
if dataset:
self.setData(dataset)
res = []
for seq in self.ds._provideSequences():
self.module.resetDerivatives()
self._calcDeriv... | Numeric check of the computed gradient for debugging purposes. | _checkGradient | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def testOnData(self, dataset=None, verbose=False):
"""Compute the MSE of the module performance on the given dataset.
If no dataset is supplied, the one passed upon Trainer initialization is
used."""
if dataset == None:
dataset = self.ds
dataset.reset()
if ve... | Compute the MSE of the module performance on the given dataset.
If no dataset is supplied, the one passed upon Trainer initialization is
used. | testOnData | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def testOnClassData(self, dataset=None, verbose=False,
return_targets=False):
"""Return winner-takes-all classification output on a given dataset.
If no dataset is given, the dataset passed during Trainer
initialization is used. If return_targets is set, also return
... | Return winner-takes-all classification output on a given dataset.
If no dataset is given, the dataset passed during Trainer
initialization is used. If return_targets is set, also return
corresponding target classes.
| testOnClassData | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def trainUntilConvergence(self, dataset=None, maxEpochs=None, verbose=None,
continueEpochs=10, validationProportion=0.25,
trainingData=None, validationData=None,
convergence_threshold=10):
"""Train the module on the datase... | Train the module on the dataset until it converges.
Return the module with the parameters that gave the minimal validation
error.
If no dataset is given, the dataset passed during Trainer
initialization is used. validationProportion is the ratio of the dataset
that is used for ... | trainUntilConvergence | python | pybrain/pybrain | pybrain/supervised/trainers/backprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/backprop.py | BSD-3-Clause |
def __init__(self, evolino_network, dataset, **kwargs):
"""
:key subPopulationSize: Size of the subpopulations.
:key nCombinations: Number of times each chromosome is built into an individual. default=1
:key nParents: Number of individuals left in a subpopulation after select... |
:key subPopulationSize: Size of the subpopulations.
:key nCombinations: Number of times each chromosome is built into an individual. default=1
:key nParents: Number of individuals left in a subpopulation after selection.
:key initialWeightRange: Range of the weights of t... | __init__ | python | pybrain/pybrain | pybrain/supervised/trainers/evolino.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/evolino.py | BSD-3-Clause |
def gaussian(x, mean, stddev):
""" return value of homogenous Gaussian at given vector point
x: vector, mean: vector, stddev: scalar """
tmp = -0.5 * sum(((x-mean)/stddev)**2)
return np.exp(tmp) / (np.power(2.*np.pi, 0.5*len(x)) * stddev) | return value of homogenous Gaussian at given vector point
x: vector, mean: vector, stddev: scalar | gaussian | python | pybrain/pybrain | pybrain/supervised/trainers/mixturedensity.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/mixturedensity.py | BSD-3-Clause |
def _calcDerivs(self, seq):
""" calculate derivatives assuming we have a Network with a MixtureDensityLayer as output """
assert isinstance(self.module.modulesSorted[-1], MixtureDensityLayer)
self.module.reset()
for time, sample in enumerate(seq):
input = samp... | calculate derivatives assuming we have a Network with a MixtureDensityLayer as output | _calcDerivs | python | pybrain/pybrain | pybrain/supervised/trainers/mixturedensity.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/mixturedensity.py | BSD-3-Clause |
def __init__(self, module, etaminus=0.5, etaplus=1.2, deltamin=1.0e-6, deltamax=5.0, delta0=0.1, **kwargs):
""" Set up training algorithm parameters, and objects associated with the trainer.
:arg module: the module whose parameters should be trained.
:key etaminus: factor by which step ... | Set up training algorithm parameters, and objects associated with the trainer.
:arg module: the module whose parameters should be trained.
:key etaminus: factor by which step width is decreased when overstepping (0.5)
:key etaplus: factor by which step width is increased when follo... | __init__ | python | pybrain/pybrain | pybrain/supervised/trainers/rprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/rprop.py | BSD-3-Clause |
def train(self):
""" Train the network for one epoch """
self.module.resetDerivatives()
errors = 0
ponderation = 0
for seq in self.ds._provideSequences():
e, p = self._calcDerivs(seq)
errors += e
ponderation += p
if self.verbose:
... | Train the network for one epoch | train | python | pybrain/pybrain | pybrain/supervised/trainers/rprop.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/rprop.py | BSD-3-Clause |
def __init__(self, svmunit, dataset, modelfile=None, plot=False):
""" Initialize data and unit to be trained, and load the model, if
provided.
The passed `svmunit` has to be an object of class :class:`SVMUnit`
that is going to be trained on the :class:`ClassificationDataSet` o... | Initialize data and unit to be trained, and load the model, if
provided.
The passed `svmunit` has to be an object of class :class:`SVMUnit`
that is going to be trained on the :class:`ClassificationDataSet` object
dataset.
Compared to FNN training we do not use a test... | __init__ | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def train(self, search=False, **kwargs):
""" Train the SVM on the dataset. For RBF kernels (the default), an optional meta-parameter search can be performed.
:key search: optional name of grid search class to use for RBF kernels: 'GridSearch' or 'GridSearchDOE'
:key log2g: base 2 log of the RB... | Train the SVM on the dataset. For RBF kernels (the default), an optional meta-parameter search can be performed.
:key search: optional name of grid search class to use for RBF kernels: 'GridSearch' or 'GridSearchDOE'
:key log2g: base 2 log of the RBF width parameter
:key log2C: base 2 log of ... | train | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def setParams(self, **kwargs):
""" Set parameters for SVM training. Apart from the ones below, you can use all parameters
defined for the LIBSVM svm_model class, see their documentation.
:key searchlog: Save a list of coordinates and the achieved CV accuracy to this file."""
if 'weight... | Set parameters for SVM training. Apart from the ones below, you can use all parameters
defined for the LIBSVM svm_model class, see their documentation.
:key searchlog: Save a list of coordinates and the achieved CV accuracy to this file. | setParams | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def __init__(self, problem, targets, cmin, cmax, cstep=None, crossval=5,
plotflag=False, maxdepth=8, searchlog='gridsearch_results.txt', **params):
""" Set up (log) grid search over the two RBF kernel parameters C and gamma.
:arg problem: the LIBSVM svm_problem to be optimized, ie. the... | Set up (log) grid search over the two RBF kernel parameters C and gamma.
:arg problem: the LIBSVM svm_problem to be optimized, ie. the input and target data
:arg targets: unfortunately, the targets used in the problem definition have to be given again here
:arg cmin: lower left corner of the l... | __init__ | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def search(self):
""" iterate successive parameter grid refinement and evaluation; adapted from LIBSVM grid search tool """
jobs = self.calculate_jobs()
scores = []
for line in jobs:
for (c, g) in line:
# run cross-validation for this point
sel... | iterate successive parameter grid refinement and evaluation; adapted from LIBSVM grid search tool | search | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def _permute_sequence(self, seq):
""" helper function to create a nice sequence of refined regular grids; from LIBSVM grid search tool """
n = len(seq)
if n <= 1: return seq
mid = int(n / 2)
left = self._permute_sequence(seq[:mid])
right = self._permute_sequence(seq[... | helper function to create a nice sequence of refined regular grids; from LIBSVM grid search tool | _permute_sequence | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def _range_f(self, begin, end, step):
""" like range, but works on non-integer too; from LIBSVM grid search tool """
seq = []
while 1:
if step > 0 and begin > end: break
if step < 0 and begin < end: break
seq.append(begin)
begin = begin + step
... | like range, but works on non-integer too; from LIBSVM grid search tool | _range_f | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def _save_points(self, res):
""" save the list of points and corresponding scores into a file """
self.resfile.write("%g, %g, %g\n" % res)
logging.info("log2C=%g, log2g=%g, res=%g" % res)
self.resfile.flush() | save the list of points and corresponding scores into a file | _save_points | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def search(self, cmin=None, cmax=None):
""" iterate parameter grid refinement and evaluation recursively """
if self.depth > self.maxdepth:
# maximum search depth reached - finish up
best = self.allPts[self.allScores.argmax(), :]
logging.info("best log2C=%12.7g, log2g... | iterate parameter grid refinement and evaluation recursively | search | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def refineGrid(self, cmin, cmax):
""" given grid boundaries, generate the corresponding DOE pattern from template"""
diff = array((cmax - cmin).tolist()*self.nPts).reshape(self.nPts, self.nPars)
return self.doepat * diff + array(cmin.tolist()*self.nPts).reshape(self.nPts, self.nPars) | given grid boundaries, generate the corresponding DOE pattern from template | refineGrid | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def _findIndex(self, point):
""" determines whether given point already exists in list of all calculated points.
raises exception if more than one point is found, returns -1 if no point is found """
if self.depth == 0: return - 1
check = self.allPts[:, 0] == point[0]
for i in ran... | determines whether given point already exists in list of all calculated points.
raises exception if more than one point is found, returns -1 if no point is found | _findIndex | python | pybrain/pybrain | pybrain/supervised/trainers/svmtrainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/svmtrainer.py | BSD-3-Clause |
def setData(self, dataset):
"""Associate the given dataset with the trainer."""
self.ds = dataset
if dataset:
assert dataset.indim == self.module.indim
assert dataset.outdim == self.module.outdim | Associate the given dataset with the trainer. | setData | python | pybrain/pybrain | pybrain/supervised/trainers/trainer.py | https://github.com/pybrain/pybrain/blob/master/pybrain/supervised/trainers/trainer.py | BSD-3-Clause |
def epsilonCheck(x, epsilon=1e-6):
"""Checks that x is in (-epsilon, epsilon)."""
epsilon = abs(epsilon)
return -epsilon < x < epsilon | Checks that x is in (-epsilon, epsilon). | epsilonCheck | python | pybrain/pybrain | pybrain/tests/helpers.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/helpers.py | BSD-3-Clause |
def buildAppropriateDataset(module):
""" build a sequential dataset with 2 sequences of 3 samples, with arndom input and target values,
but the appropriate dimensions to be used on the provided module. """
if module.sequential:
d = SequentialDataSet(module.indim, module.outdim)
for dummy in ... | build a sequential dataset with 2 sequences of 3 samples, with arndom input and target values,
but the appropriate dimensions to be used on the provided module. | buildAppropriateDataset | python | pybrain/pybrain | pybrain/tests/helpers.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/helpers.py | BSD-3-Clause |
def gradientCheck(module, tolerance=0.0001, dataset=None):
""" check the gradient of a module with a randomly generated dataset,
(and, in the case of a network, determine which modules contain incorrect derivatives). """
if module.paramdim == 0:
print('Module has no parameters')
return True
... | check the gradient of a module with a randomly generated dataset,
(and, in the case of a network, determine which modules contain incorrect derivatives). | gradientCheck | python | pybrain/pybrain | pybrain/tests/helpers.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/helpers.py | BSD-3-Clause |
def xmlInvariance(n, forwardpasses = 1):
""" try writing a network to an xml file, reading it, rewrite it, reread it, and compare
if the result looks the same (compare string representation, and forward processing
of some random inputs) """
# We only use this for file creation.
tmpfile = tempfile.Na... | try writing a network to an xml file, reading it, rewrite it, reread it, and compare
if the result looks the same (compare string representation, and forward processing
of some random inputs) | xmlInvariance | python | pybrain/pybrain | pybrain/tests/helpers.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/helpers.py | BSD-3-Clause |
def testInterface(algo):
""" Tests whether the algorithm is properly implementing the
correct Blackbox-optimization interface."""
# without any arguments, initialization has to work
emptyalgo = algo()
try:
# but not learning
emptyalgo.learn(0)
return "Failed to throw missing ... | Tests whether the algorithm is properly implementing the
correct Blackbox-optimization interface. | testInterface | python | pybrain/pybrain | pybrain/tests/optimizationtest.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/optimizationtest.py | BSD-3-Clause |
def testContinuousInterface(algo):
""" Test the specifics for the interface for ContinuousOptimizers """
if not issubclass(algo, bbo.ContinuousOptimizer):
return True
# list starting points are internally converted to arrays
x = algo(sf, xlist2)
assert isinstance(x.bestEvaluable, ndarray), '... | Test the specifics for the interface for ContinuousOptimizers | testContinuousInterface | python | pybrain/pybrain | pybrain/tests/optimizationtest.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/optimizationtest.py | BSD-3-Clause |
def testMinMax(algo):
""" Verify that the algorithm is doing the minimization/maximization consistently. """
if (issubclass(algo, bbo.TopologyOptimizer)
or algo == allopts.StochasticHillClimber):
# TODO
return True
xa1[0] = 2
evalx = sf(xa1)
amax1 = algo(sf, xa1, minimize=F... | Verify that the algorithm is doing the minimization/maximization consistently. | testMinMax | python | pybrain/pybrain | pybrain/tests/optimizationtest.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/optimizationtest.py | BSD-3-Clause |
def testImport(module_name):
"""Tell wether a module can be imported.
This function has a cache, so modules are only tested once on
importability.
"""
try:
return testImport.cache[module_name]
except KeyError:
try:
__import__(module_name)
except ImportError:
... | Tell wether a module can be imported.
This function has a cache, so modules are only tested once on
importability.
| testImport | python | pybrain/pybrain | pybrain/tests/runtests.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/runtests.py | BSD-3-Clause |
def missingDependencies(target_module):
"""Returns a list of dependencies of the module that the current
interpreter cannot import.
This does not inspect the code, but instead check for a list of strings
called _dependencies in the target_module. This list should contain module
names that the modul... | Returns a list of dependencies of the module that the current
interpreter cannot import.
This does not inspect the code, but instead check for a list of strings
called _dependencies in the target_module. This list should contain module
names that the module depends on. | missingDependencies | python | pybrain/pybrain | pybrain/tests/runtests.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/runtests.py | BSD-3-Clause |
def getSubDirectories(testdir):
"""Recursively builds a list of all subdirectories in the test suite."""
subdirs = [os.path.join(testdir,d) for d in
filter(os.path.isdir,[os.path.join(testdir,dd) for dd in os.listdir(testdir)])]
for d in copy(subdirs):
subdirs.extend(getSubDirect... | Recursively builds a list of all subdirectories in the test suite. | getSubDirectories | python | pybrain/pybrain | pybrain/tests/runtests.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/runtests.py | BSD-3-Clause |
def make_test_suite():
"""Load unittests placed in pybrain/tests/unittests, then return a
TestSuite object of those."""
# [...]/pybrain/pybrain [cut] /tests/runtests.py
path = os.path.abspath(__file__).rsplit(os.sep+'tests', 1)[0]
sys.path.append(path.rstrip('pybrain'))
top_testdir = os.path.j... | Load unittests placed in pybrain/tests/unittests, then return a
TestSuite object of those. | make_test_suite | python | pybrain/pybrain | pybrain/tests/runtests.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/runtests.py | BSD-3-Clause |
def runModuleTestSuite(module):
"""Runs a test suite for all local tests."""
suite = TestSuite([TestLoader().loadTestsFromModule(module)])
# Add local doctests
optionflags = ELLIPSIS | NORMALIZE_WHITESPACE | REPORT_ONLY_FIRST_FAILURE | IGNORE_EXCEPTION_DETAIL
try:
suite.addTest(DocTestSuit... | Runs a test suite for all local tests. | runModuleTestSuite | python | pybrain/pybrain | pybrain/tests/testsuites.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/testsuites.py | BSD-3-Clause |
def buildSharedCrossedNetwork():
""" build a network with shared connections. Two hidden modules are
symmetrically linked, but to a different input neuron than the
output neuron. The weights are random. """
N = FeedForwardNetwork('shared-crossed')
h = 1
a = LinearLayer(2, name = 'a')
b = Lin... | build a network with shared connections. Two hidden modules are
symmetrically linked, but to a different input neuron than the
output neuron. The weights are random. | buildSharedCrossedNetwork | python | pybrain/pybrain | pybrain/tests/unittests/structure/connections/test_shared_connections.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/unittests/structure/connections/test_shared_connections.py | BSD-3-Clause |
def buildSimpleBorderSwipingNet(size = 3, dim = 3, hsize = 1, predefined = {}):
""" build a simple swiping network,of given size and dimension, using linear inputs and output"""
# assuming identical size in all dimensions
dims = tuple([size]*dim)
# also includes one dimension for the swipes
hdims = ... | build a simple swiping network,of given size and dimension, using linear inputs and output | buildSimpleBorderSwipingNet | python | pybrain/pybrain | pybrain/tests/unittests/structure/networks/test_borderswipingnetwork.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/unittests/structure/networks/test_borderswipingnetwork.py | BSD-3-Clause |
def buildCyclicNetwork(recurrent):
""" build a cyclic network with 4 modules
:key recurrent: make one of the connections recurrent """
Network = RecurrentNetwork if recurrent else FeedForwardNetwork
N = Network('cyc')
a = LinearLayer(1, name='a')
b = LinearLayer(2, name='b')
c = LinearLayer... | build a cyclic network with 4 modules
:key recurrent: make one of the connections recurrent | buildCyclicNetwork | python | pybrain/pybrain | pybrain/tests/unittests/structure/networks/test_cyclic_network.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/unittests/structure/networks/test_cyclic_network.py | BSD-3-Clause |
def buildMixedNestedNetwork():
""" build a nested network with the inner one being a ffn and the outer one being recurrent. """
N = RecurrentNetwork('outer')
a = LinearLayer(1, name='a')
b = LinearLayer(2, name='b')
c = buildNetwork(2, 3, 1)
c.name = 'inner'
N.addInputModule(a)
N.addModu... | build a nested network with the inner one being a ffn and the outer one being recurrent. | buildMixedNestedNetwork | python | pybrain/pybrain | pybrain/tests/unittests/structure/networks/test_nested_ffn_and_rnn.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/unittests/structure/networks/test_nested_ffn_and_rnn.py | BSD-3-Clause |
def buildDecomposableNetwork():
""" three hidden neurons, with 2 in- and 2 outconnections each. """
n = buildNetwork(2, 3, 2, bias = False)
ndc = NeuronDecomposableNetwork.convertNormalNetwork(n)
# set all the weights to 1
ndc._setParameters(ones(12))
return ndc | three hidden neurons, with 2 in- and 2 outconnections each. | buildDecomposableNetwork | python | pybrain/pybrain | pybrain/tests/unittests/structure/networks/test_network_decomposition.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/unittests/structure/networks/test_network_decomposition.py | BSD-3-Clause |
def buildSomeConnections(modules):
""" add a connection from every second to every third module """
res = []
for i in range(len(modules)//3-1):
res.append(FullConnection(modules[i*2], modules[i*3+1]))
return res | add a connection from every second to every third module | buildSomeConnections | python | pybrain/pybrain | pybrain/tests/unittests/structure/networks/test_network_sort.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tests/unittests/structure/networks/test_network_sort.py | BSD-3-Clause |
def convertSequenceToTimeWindows(DSseq, NewClass, winsize):
""" Converts a sequential classification dataset into time windows of fixed length.
Assumes the correct class is given at the last timestep of each sequence. Incomplete windows at the
sequence end are pruned. No overlap between windows.
:arg D... | Converts a sequential classification dataset into time windows of fixed length.
Assumes the correct class is given at the last timestep of each sequence. Incomplete windows at the
sequence end are pruned. No overlap between windows.
:arg DSseq: the sequential data set to cut up
:arg winsize: size of t... | convertSequenceToTimeWindows | python | pybrain/pybrain | pybrain/tools/datasettools.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/datasettools.py | BSD-3-Clause |
def windowSequenceEval(DS, winsz, result):
""" take results of a window-based classification and assess/plot them on the sequence
WARNING: NOT TESTED!"""
si_old = 0
idx = 0
x = []
y = []
seq_res = []
for i, si in enumerate(DS['sequence_index'][1:].astype(int)):
tar = DS['target']... | take results of a window-based classification and assess/plot them on the sequence
WARNING: NOT TESTED! | windowSequenceEval | python | pybrain/pybrain | pybrain/tools/datasettools.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/datasettools.py | BSD-3-Clause |
def normalize(self, ds, field='input'):
""" normalize dataset or vector wrt. to stored min and max """
if self.dim <= 0:
raise IndexError("No normalization parameters defined!")
dsdim = ds[field].shape[1]
if self.dim != dsdim:
raise IndexError("Dimension of normal... | normalize dataset or vector wrt. to stored min and max | normalize | python | pybrain/pybrain | pybrain/tools/datasettools.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/datasettools.py | BSD-3-Clause |
def getAllFilesIn(dir, tag='', extension='.pickle'):
""" return a list of all filenames in the specified directory
(with the given tag and/or extension). """
allfiles = os.listdir(dir)
res = []
for f in allfiles:
if f[-len(extension):] == extension and f[:len(tag)] == tag:
res.ap... | return a list of all filenames in the specified directory
(with the given tag and/or extension). | getAllFilesIn | python | pybrain/pybrain | pybrain/tools/filehandling.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/filehandling.py | BSD-3-Clause |
def selectSome(strings, requiredsubstrings=[], requireAll=True):
""" Filter the list of strings to only contain those that have at least
one of the required substrings. """
if len(requiredsubstrings) == 0:
return strings
res = []
for s in strings:
if requireAll:
bad = Fal... | Filter the list of strings to only contain those that have at least
one of the required substrings. | selectSome | python | pybrain/pybrain | pybrain/tools/filehandling.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/filehandling.py | BSD-3-Clause |
def pickleReadDict(name):
""" pickle-read a (default: dictionnary) variable from a file """
try:
f = open(name + '.pickle')
val = pickle.load(f)
f.close()
except Exception as e:
print(('Nothing read from', name, ':', str(e)))
val = {}
return val | pickle-read a (default: dictionnary) variable from a file | pickleReadDict | python | pybrain/pybrain | pybrain/tools/filehandling.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/filehandling.py | BSD-3-Clause |
def calcFisherInformation(sigma, invSigma=None, factorSigma=None):
""" Compute the exact Fisher Information Matrix of a Gaussian distribution,
given its covariance matrix.
Returns a list of the diagonal blocks. """
if invSigma == None:
invSigma = inv(sigma)
if factorSigma == None:
fa... | Compute the exact Fisher Information Matrix of a Gaussian distribution,
given its covariance matrix.
Returns a list of the diagonal blocks. | calcFisherInformation | python | pybrain/pybrain | pybrain/tools/fisher.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/fisher.py | BSD-3-Clause |
def calcInvFisher(sigma, invSigma=None, factorSigma=None):
""" Efficiently compute the exact inverse of the FIM of a Gaussian.
Returns a list of the diagonal blocks. """
if invSigma == None:
invSigma = inv(sigma)
if factorSigma == None:
factorSigma = cholesky(sigma)
dim = sigma.shape... | Efficiently compute the exact inverse of the FIM of a Gaussian.
Returns a list of the diagonal blocks. | calcInvFisher | python | pybrain/pybrain | pybrain/tools/fisher.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/fisher.py | BSD-3-Clause |
def semilinear(x):
""" This function ensures that the values of the array are always positive. It is
x+1 for x=>0 and exp(x) for x<0. """
try:
# assume x is a numpy array
shape = x.shape
x.flatten()
x = x.tolist()
except AttributeError:
# no, it wasn't: build ... | This function ensures that the values of the array are always positive. It is
x+1 for x=>0 and exp(x) for x<0. | semilinear | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def semilinearPrime(x):
""" This function is the first derivative of the semilinear function (above).
It is needed for the backward pass of the module. """
try:
# assume x is a numpy array
shape = x.shape
x.flatten()
x = x.tolist()
except AttributeError:
# no,... | This function is the first derivative of the semilinear function (above).
It is needed for the backward pass of the module. | semilinearPrime | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def ranking(R):
""" Produces a linear ranking of the values in R. """
l = sorted(list(enumerate(R)), cmp=lambda a, b: cmp(a[1], b[1]))
l = sorted(list(enumerate(l)), cmp=lambda a, b: cmp(a[1], b[1]))
return array([kv[0] for kv in l]) | Produces a linear ranking of the values in R. | ranking | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def expln(x):
""" This continuous function ensures that the values of the array are always positive.
It is ln(x+1)+1 for x >= 0 and exp(x) for x < 0. """
def f(val):
if val < 0:
# exponential function for x < 0
return exp(val)
else:
# natural log funct... | This continuous function ensures that the values of the array are always positive.
It is ln(x+1)+1 for x >= 0 and exp(x) for x < 0. | expln | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def explnPrime(x):
""" This function is the first derivative of the expln function (above).
It is needed for the backward pass of the module. """
def f(val):
if val < 0:
# exponential function for x<0
return exp(val)
else:
# linear function for x>=0
... | This function is the first derivative of the expln function (above).
It is needed for the backward pass of the module. | explnPrime | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def multivariateNormalPdf(z, x, sigma):
""" The pdf of a multivariate normal distribution (not in scipy).
The sample z and the mean x should be 1-dim-arrays, and sigma a square 2-dim-array. """
assert len(z.shape) == 1 and len(x.shape) == 1 and len(x) == len(z) and sigma.shape == (len(x), len(z))
tmp = ... | The pdf of a multivariate normal distribution (not in scipy).
The sample z and the mean x should be 1-dim-arrays, and sigma a square 2-dim-array. | multivariateNormalPdf | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def simpleMultivariateNormalPdf(z, detFactorSigma):
""" Assuming z has been transformed to a mean of zero and an identity matrix of covariances.
Needs to provide the determinant of the factorized (real) covariance matrix. """
dim = len(z)
return exp(-0.5 * dot(z, z)) / (power(2.0 * pi, dim / 2.) * detFa... | Assuming z has been transformed to a mean of zero and an identity matrix of covariances.
Needs to provide the determinant of the factorized (real) covariance matrix. | simpleMultivariateNormalPdf | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def multivariateCauchy(mu, sigma, onlyDiagonal=True):
""" Generates a sample according to a given multivariate Cauchy distribution. """
if not onlyDiagonal:
u, s, d = svd(sigma)
coeffs = sqrt(s)
else:
coeffs = diag(sigma)
r = rand(len(mu))
res = coeffs * tan(pi * (r - 0.5))
... | Generates a sample according to a given multivariate Cauchy distribution. | multivariateCauchy | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def approxChiFunction(dim):
""" Returns Chi (expectation of the length of a normal random vector)
approximation according to: Ostermeier 1997. """
dim = float(dim)
return sqrt(dim) * (1 - 1 / (4 * dim) + 1 / (21 * dim ** 2)) | Returns Chi (expectation of the length of a normal random vector)
approximation according to: Ostermeier 1997. | approxChiFunction | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def sqrtm(M):
""" Returns the symmetric semi-definite positive square root of a matrix. """
r = real_if_close(expm(0.5 * logm(M)), 1e-8)
return (r + r.T) / 2 | Returns the symmetric semi-definite positive square root of a matrix. | sqrtm | python | pybrain/pybrain | pybrain/tools/functions.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/functions.py | BSD-3-Clause |
def __init__(self, min_params, max_params, n_steps=7, **kwargs):
""" :key min_params: Tuple of two elements specifying the minima
of the two metaparameters
:key max_params: Tuple of two elements specifying the minima
of the two metaparame... | :key min_params: Tuple of two elements specifying the minima
of the two metaparameters
:key max_params: Tuple of two elements specifying the minima
of the two metaparameters
:key max_param: Tuple of two elements, specifying the numb... | __init__ | python | pybrain/pybrain | pybrain/tools/gridsearch.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/gridsearch.py | BSD-3-Clause |
def search(self):
""" The main search method, that validates all calculated metaparameter
settings (=jobs) by calling the abstract _validate() method.
After enough new jobs were validated in order to visualize a grid,
the _onStep() callback method is called.
"""
... | The main search method, that validates all calculated metaparameter
settings (=jobs) by calling the abstract _validate() method.
After enough new jobs were validated in order to visualize a grid,
the _onStep() callback method is called.
| search | python | pybrain/pybrain | pybrain/tools/gridsearch.py | https://github.com/pybrain/pybrain/blob/master/pybrain/tools/gridsearch.py | BSD-3-Clause |
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