def __init__(self):
self.book = 0
self.emails = ""
# Generate the numbers and insert them into the numbers array in the order we want them to appear in the game
num = NumberGenerator()
num.generate_numbers()
num.fill_lists()
num.view_numbers(num.numbers)
self.numbers = [
num.first, num.second, num.third, num.fourth, num.fifth, num.sixth
]
def getNumberGenerator(genType, minValue, maxValue, seed, **kwargs):
if genType is Const.RND_RU:
valGenerator = NumberGenerator(minValue, maxValue, seed)
elif genType is Const.RND_SET:
probLaw = kwargs["probLaw"]
valGenerator = CustomDiscreteNumberGenerator(probLaw, seed)
elif genType is Const.RND_GAUSS:
if "outRange" in kwargs:
outRange = kwargs["outRange"]
valGenerator = GaussianNumberGenerator(minValue, maxValue, seed,
outRange)
else:
valGenerator = GaussianNumberGenerator(minValue, maxValue, seed)
return valGenerator
class SparsityDecoratorFG(FilterGenerator):
def __init__(self, filterGenerator, sparseProb, seed=None):
self._fg = filterGenerator
self._numGen = NumberGenerator(0, 1, seed)
self._prob = sparseProb
def next(self):
filt = self._fg.next()
height, width = filt.shape
for h in xrange(height):
for w in xrange(width):
if self._numGen.getNumber(0, 1) < self._prob:
filt[h][w] = 0
return filt
class SparsityDecoratorFG(FilterGenerator):
def __init__(self, filterGenerator, sparseProb, seed=None):
self._fg = filterGenerator
self._numGen = NumberGenerator(0, 1, seed)
self._prob = sparseProb
def next(self):
filt = self._fg.next()
height, width = filt.shape
for h in xrange(height):
for w in xrange(width):
if self._numGen.getNumber(0, 1) < self._prob:
filt[h][w] = 0
return filt
if __name__ == "__main__":
test = True
if test:
imgpath = "lena.png"
from NumberGenerator import NumberGenerator
try:
import Image
except:
from PIL import Image
img = Image.open(imgpath)
width, height = img.size
swExt = SubWindowExtractor(0.5, 1., 256, 250,
SubWindowExtractor.INTERPOLATION_BILINEAR,
NumberGenerator())
sw1, box1 = swExt.extract(img)
mExt = MultiSWExtractor(swExt, 10)
mExt.refresh(width, height)
sws = mExt.extract(img)
swExt = FixImgSWExtractor(width, height, 0.5, 1., 256, 250,
SubWindowExtractor.INTERPOLATION_BILINEAR,
NumberGenerator())
sw2, box2 = swExt.extract(img)
mExt = MultiSWExtractor(swExt, 10)
img = np.array(imgPil)
#CONVOLUTIONAL EXTRACTOR
#Filter generator
filterValGenerator = IntegerUniformGenerator(-5, 5)
filterSizeGenerator = OddUniformGenerator(3, 10)
baseFilterGenerator = FilterGenerator(filterValGenerator,
filterSizeGenerator)
filterGenerator = Finite3SameFilter(baseFilterGenerator, 6)
#Convolver
convolver = RGBConvolver()
#SubWindowExtractor
subwindowTargetWidth = 200
subwindowTargetHeight = 200
swNumGenerator = NumberGenerator()
swExtractor = SubWindowExtractor(
0.5, 1., subwindowTargetWidth, subwindowTargetHeight,
SubWindowExtractor.INTERPOLATION_BILINEAR, swNumGenerator)
multiSWExtractor = MultiSWExtractor(swExtractor, 4)
#Aggregator
aggregator = AverageAggregator(10, 10, subwindowTargetWidth,
subwindowTargetHeight)
convolutionalExtractor = ConvolutionalExtractor(
filterGenerator, convolver, multiSWExtractor, aggregator)
res = convolutionalExtractor.extract(img)
def coordinatorPixitFactory(
nbSubwindows=10,
subwindowMinSizeRatio=0.5, subwindowMaxSizeRatio=1.,
subwindowTargetWidth=16, subwindowTargetHeight=16,
fixedSize=False,
subwindowInterpolation=SubWindowExtractor.INTERPOLATION_BILINEAR,
nbJobs=-1, verbosity=10, tempFolder=None,
random=True):
"""
Factory method to create :class:`PixitCoordinator`
Parameters
----------
nbSubwindows : int >= 0 (default : 10)
The number of subwindow to extract
subwindowMinSizeRatio : float > 0 (default : 0.5)
The minimum size of a subwindow expressed as the ratio of the size
of the original image
subwindowMaxSizeRatio : float : subwindowMinSizeRatio
<= subwindowMaxSizeRatio <= 1 (default : 1.)
The maximim size of a subwindow expressed as the ratio of the size
of the original image
subwindowTargetWidth : int > 0 (default : 16)
The width of the subwindows after reinterpolation
subwindowTargetHeight : int > 0 (default : 16)
The height of the subwindows after reinterpolation
fixedSize : boolean (default : False)
Whether to use fixe size subwindow. If False, subwindows are drawn
randomly. If True, the target size is use as the subwindow size and
only the position is drawn randomly
subwindowInterpolation : int (default :
SubWindowExtractor.INTERPOLATION_BILINEAR)
The subwindow reinterpolation algorithm. For more information, see
:class:`SubWindowExtractor`
nbJobs : int >0 or -1 (default : -1)
The number of process to spawn for parallelizing the computation.
If -1, the maximum number is selected. See also :mod:`Joblib`.
verbosity : int >= 0 (default : 10)
The verbosity level
tempFolder : string (directory path) (default : None)
The temporary folder used for memmap. If none, some default folder
will be use (see the :class:`ParallelCoordinator`)
random : bool (default : True)
Whether to use randomness or use a predefined seed
Return
------
coordinator : :class:`Coordinator`
The PixitCoordinator (possibly decorated) corresponding to the set
of parameters
Notes
-----
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformely).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
swngSeed = 0
#Randomness
if random:
swngSeed = None
#SubWindowExtractor
swNumGenerator = NumberGenerator(seed=swngSeed)
if fixedSize:
swExtractor = FixTargetSWExtractor(subwindowTargetWidth,
subwindowTargetHeight,
subwindowInterpolation,
swNumGenerator)
else:
swExtractor = SubWindowExtractor(subwindowMinSizeRatio,
subwindowMaxSizeRatio,
subwindowTargetWidth,
subwindowTargetHeight,
subwindowInterpolation,
swNumGenerator)
multiSWExtractor = MultiSWExtractor(swExtractor, nbSubwindows, True)
#FEATURE EXTRACTOR
featureExtractor = ImageLinearizationExtractor()
#LOGGER
autoFlush = verbosity >= 45
logger = ProgressLogger(StandardLogger(autoFlush=autoFlush,
verbosity=verbosity))
#COORDINATOR
coordinator = PixitCoordinator(multiSWExtractor, featureExtractor, logger,
verbosity)
if nbJobs != 1:
coordinator.parallelize(nbJobs, tempFolder)
return coordinator
def coordinatorCompressRandConvFactory(
nbFilters=5,
filterMinVal=-1, filterMaxVal=1,
filterMinSize=1, filterMaxSize=17,
filterNormalisation=FilterGenerator.NORMALISATION_MEANVAR,
poolings=[(3, 3, Const.POOLING_AGGREG_AVG)],
nbSubwindows=10,
subwindowMinSizeRatio=0.5, subwindowMaxSizeRatio=1.,
subwindowTargetWidth=16, subwindowTargetHeight=16,
subwindowInterpolation=SubWindowExtractor.INTERPOLATION_BILINEAR,
includeOriginalImage=False,
compressorType="Sampling", nbCompressedFeatures=1,
compressOriginalImage=True,
nbJobs=-1, verbosity=10, tempFolder=None,
random=True):
"""
Factory method to create :class:`RandConvCoordinator` tuned for RGB images
Parameters
----------
nbFilters : int >= 0 (default : 5)
The number of filter
filterMinVal : float (default : -1)
The minimum value of a filter component
filterMaxVal : float : filterMinVal <= filterMaxVal (default : 1)
The maximum value of a filter component
filterMinSize : int >= 0 : odd number (default : 1)
The minimum size of a filter
filterMaxSize : int >= 0 : odd number s.t. filterMinSize <= filterMaxSize
(default : 1)
The maximum size of a filter
filterNormalisation : int (default : FilterGenerator.NORMALISATION_MEANVAR)
The filter normalisation policy. See also :class:`FilterGenerator`
poolings : iterable of triple (height, width, policy) (default :
[(3, 3, Const.POOLING_AGGREG_AVG)])
A list of parameters to instanciate the according :class:`Pooler`
height : int > 0
the height of the neighborhood window
width : int > 0
the width of the neighborhood window
policy : int in {Const.POOLING_NONE, Const.POOLING_AGGREG_MIN,
Const.POOLING_AGGREG_AVG, Const.POOLING_AGGREG_MAX,
Const.POOLING_CONV_MIN, Const.POOLING_CONV_AVG, Const.POOLING_CONV_MAX}
nbSubwindows : int >= 0 (default : 10)
The number of subwindow to extract
subwindowMinSizeRatio : float > 0 (default : 0.5)
The minimum size of a subwindow expressed as the ratio of the size
of the original image
subwindowMaxSizeRatio : float : subwindowMinSizeRatio
<= subwindowMaxSizeRatio <= 1 (default : 1.)
The maximim size of a subwindow expressed as the ratio of the size
of the original image
subwindowTargetWidth : int > 0 (default : 16)
The width of the subwindows after reinterpolation
subwindowTargetHeight : int > 0 (default : 16)
The height of the subwindows after reinterpolation
subwindowInterpolation : int (default :
SubWindowExtractor.INTERPOLATION_BILINEAR)
The subwindow reinterpolation algorithm. For more information, see
:class:`SubWindowExtractor`
includeOriginalImage : boolean (default : False)
Whether or not to include the original image in the subwindow
extraction process
compressorType : str (default : "Sampling")
The type of compressor to use. One of the {"Sampling", "PCA"}
nbCompressedFeatures : int > 0 (default : 1)
The number features per filter after compression. It should be
inferior to the number of features per filter traditionally output by
the :class:`randConvCoordinator`
compressOriginalImage : boolean (default : True)
Whether to compress the original (if included) as well as the other
features
nbJobs : int >0 or -1 (default : -1)
The number of process to spawn for parallelizing the computation.
If -1, the maximum number is selected. See also :mod:`Joblib`.
verbosity : int >= 0 (default : 10)
The verbosity level
tempFolder : string (directory path) (default : None)
The temporary folder used for memmap. If none, some default folder
will be use (see the :class:`ParallelCoordinator`)
random : bool (default : True)
Whether to use randomness or use a predefined seed
Return
------
coordinator : :class:`Coordinator`
The RandConvCoordinator (possibly decorated) corresponding to the
set of parameters
Notes
-----
- Filter generator
Base instance of :class:`Finite3SameFilter` with a base instance of
:class:`NumberGenerator` for the values and
:class:`OddUniformGenerator` for the sizes
- Filter size
The filter are square (same width as height)
- Convolver
Base instance of :class:`RGBConvolver`
- Aggregator
Base instance of :class:`AverageAggregator`
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformely).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
#RANDOMNESS
swngSeed = 0
filtValGenSeed = 1
filtSizeGenSeed = 2
if random is None:
swngSeed = None
filtValGenSeed = None
filtSizeGenSeed = None
#CONVOLUTIONAL EXTRACTOR
#Filter generator
filterValGenerator = NumberGenerator(filterMinVal, filterMaxVal,
seed=filtValGenSeed)
filterSizeGenerator = OddUniformGenerator(filterMinSize, filterMaxSize,
seed=filtSizeGenSeed)
baseFilterGenerator = FilterGenerator(filterValGenerator,
filterSizeGenerator,
normalisation=filterNormalisation)
filterGenerator = Finite3SameFilter(baseFilterGenerator, nbFilters)
#Convolver
convolver = RGBConvolver()
#Aggregator
poolers = []
for height, width, policy in poolings:
if policy == Const.POOLING_NONE:
poolers.append(IdentityPooler())
elif policy == Const.POOLING_AGGREG_AVG:
poolers.append(AverageAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_AGGREG_MAX:
poolers.append(MaximumAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_AGGREG_MIN:
poolers.append(MinimumAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_CONV_MIN:
poolers.append(ConvMinPooler(height, width))
elif policy == Const.POOLING_CONV_AVG:
poolers.append(ConvAvgPooler(height, width))
elif policy == Const.POOLING_CONV_MAX:
poolers.append(ConvMaxPooler(height, width))
multiPooler = MultiPooler(poolers)
#SubWindowExtractor
swNumGenerator = NumberGenerator(seed=swngSeed)
swExtractor = SubWindowExtractor(subwindowMinSizeRatio,
subwindowMaxSizeRatio,
subwindowTargetWidth,
subwindowTargetHeight,
subwindowInterpolation, swNumGenerator)
multiSWExtractor = MultiSWExtractor(swExtractor, nbSubwindows, False)
#ConvolutionalExtractor
convolutionalExtractor = ConvolutionalExtractor(filterGenerator,
convolver,
multiSWExtractor,
multiPooler,
includeOriginalImage)
#FEATURE EXTRACTOR
featureExtractor = ImageLinearizationExtractor()
#LOGGER
autoFlush = verbosity >= 45
logger = ProgressLogger(StandardLogger(autoFlush=autoFlush,
verbosity=verbosity))
#COMPRESSOR
if compressorType == "PCA":
compressorFactory = PCAFactory(nbCompressedFeatures)
else:
#"Sampling"
compressorFactory = SamplerFactory(nbCompressedFeatures)
#COORDINATOR
coordinator = CompressRandConvCoordinator(convolutionalExtractor,
featureExtractor,
compressorFactory,
compressOriginalImage,
logger, verbosity)
if nbJobs != 1:
coordinator.parallelize(nbJobs, tempFolder)
return coordinator
def customRandConvFactory(
poolings=[(3, 3, Const.POOLING_AGGREG_AVG)],
nbSubwindows=10,
subwindowMinSizeRatio=0.5, subwindowMaxSizeRatio=1.,
subwindowTargetWidth=16, subwindowTargetHeight=16,
subwindowInterpolation=SubWindowExtractor.INTERPOLATION_BILINEAR,
includeOriginalImage=False,
nbJobs=-1, verbosity=10, tempFolder=None,
random=True):
"""
Factory method to create :class:`RandConvCoordinator` tuned for RGB images
using predefined well-known filters
Parameters
----------
poolings : iterable of triple (height, width, policy) (default :
[(3, 3, Const.POOLING_AGGREG_AVG)])
A list of parameters to instanciate the according :class:`Pooler`
height : int > 0
the height of the neighborhood window
width : int > 0
the width of the neighborhood window
policy : int in {Const.POOLING_NONE, Const.POOLING_AGGREG_MIN,
Const.POOLING_AGGREG_AVG, Const.POOLING_AGGREG_MAX,
Const.POOLING_CONV_MIN, Const.POOLING_CONV_AVG, Const.POOLING_CONV_MAX}
nbSubwindows : int >= 0 (default : 10)
The number of subwindow to extract
subwindowMinSizeRatio : float > 0 (default : 0.5)
The minimum size of a subwindow expressed as the ratio of the size
of the original image
subwindowMaxSizeRatio : float : subwindowMinSizeRatio
<= subwindowMaxSizeRatio <= 1 (default : 1.)
The maximim size of a subwindow expressed as the ratio of the size
of the original image
subwindowTargetWidth : int > 0 (default : 16)
The width of the subwindows after reinterpolation
subwindowTargetHeight : int > 0 (default : 16)
The height of the subwindows after reinterpolation
subwindowInterpolation : int (default :
SubWindowExtractor.INTERPOLATION_BILINEAR)
The subwindow reinterpolation algorithm. For more information, see
:class:`SubWindowExtractor`
includeOriginalImage : boolean (default : False)
Whether or not to include the original image in the subwindow
extraction process
nbJobs : int >0 or -1 (default : -1)
The number of process to spawn for parallelizing the computation.
If -1, the maximum number is selected. See also :mod:`Joblib`.
verbosity : int >= 0 (default : 10)
The verbosity level
tempFolder : string (directory path) (default : None)
The temporary folder used for memmap. If none, some default folder
will be use (see the :class:`ParallelCoordinator`)
random : bool (default : True)
Whether to use randomness or use a predefined seed
Return
------
coordinator : :class:`Coordinator`
The RandConvCoordinator corresponding to the
set of parameters
Notes
-----
- Convolver
Base instance of :class:`RGBConvolver`
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformly).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
#RANDOMNESS
swngSeed = 0
if random is None:
swngSeed = None
#CONVOLUTIONAL EXTRACTOR
filterGenerator = customFinite3sameFilter()
#Convolver
convolver = RGBConvolver()
#Aggregator
poolers = []
for height, width, policy in poolings:
if policy == Const.POOLING_NONE:
poolers.append(IdentityPooler())
elif policy == Const.POOLING_AGGREG_AVG:
poolers.append(AverageAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_AGGREG_MAX:
poolers.append(MaximumAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_AGGREG_MIN:
poolers.append(MinimumAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_CONV_MIN:
poolers.append(ConvMinPooler(height, width))
elif policy == Const.POOLING_CONV_AVG:
poolers.append(ConvAvgPooler(height, width))
elif policy == Const.POOLING_CONV_MAX:
poolers.append(ConvMaxPooler(height, width))
multiPooler = getMultiPoolers(subwindowTargetHeight, subwindowTargetWidth)
#SubWindowExtractor
swNumGenerator = NumberGenerator(seed=swngSeed)
swExtractor = SubWindowExtractor(subwindowMinSizeRatio,
subwindowMaxSizeRatio,
subwindowTargetWidth,
subwindowTargetHeight,
subwindowInterpolation, swNumGenerator)
multiSWExtractor = MultiSWExtractor(swExtractor, nbSubwindows, False)
#ConvolutionalExtractor
convolutionalExtractor = ConvolutionalExtractor(filterGenerator,
convolver,
multiSWExtractor,
multiPooler,
includeOriginalImage)
#FEATURE EXTRACTOR
featureExtractor = ImageLinearizationExtractor()
#LOGGER
autoFlush = verbosity >= 45
logger = ProgressLogger(StandardLogger(autoFlush=autoFlush,
verbosity=verbosity))
#COORDINATOR
coordinator = RandConvCoordinator(convolutionalExtractor, featureExtractor,
logger, verbosity)
if nbJobs != 1:
coordinator.parallelize(nbJobs, tempFolder)
return coordinator
def coordinatorRandConvFactory(
nbFilters=5,
filterPolicy=(Const.FGEN_ZEROPERT,
{"minSize": 2, "maxSize": 32, "minVal": -1, "maxVal": 1,
"valGen": Const.RND_RU,
"normalization": FilterGenerator.NORMALISATION_MEANVAR}),
poolings=[(3, 3, Const.POOLING_AGGREG_AVG)],
extractor=(Const.FEATEXT_ALL, {}),
nbSubwindows=10,
subwindowMinSizeRatio=0.5, subwindowMaxSizeRatio=1.,
subwindowTargetWidth=16, subwindowTargetHeight=16,
subwindowInterpolation=SubWindowExtractor.INTERPOLATION_BILINEAR,
includeOriginalImage=False,
nbJobs=-1, verbosity=10, tempFolder=None,
random=True):
"""
Factory method to create :class:`RandConvCoordinator` tuned for RGB images
Parameters
----------
nbFilters : int >= 0 (default : 5)
The number of filter
filterPolicy : pair (policyType, parameters)
policyType : one of Const.FGEN_*
The type of filter generation policy to use
parameters : dict
The parameter dictionnary to forward to :func:`getFilterGenerator`
poolings : iterable of triple (height, width, policy) (default :
[(3, 3, Const.POOLING_AGGREG_AVG)])
A list of parameters to instanciate the according :class:`Pooler`
height : int > 0
the height of the neighborhood window
width : int > 0
the width of the neighborhood window
policy : int in {Const.POOLING_NONE, Const.POOLING_AGGREG_MIN,
Const.POOLING_AGGREG_AVG, Const.POOLING_AGGREG_MAX,
Const.POOLING_CONV_MIN, Const.POOLING_CONV_AVG, Const.POOLING_CONV_MAX}
nbSubwindows : int >= 0 (default : 10)
The number of subwindow to extract
subwindowMinSizeRatio : float > 0 (default : 0.5)
The minimum size of a subwindow expressed as the ratio of the size
of the original image
subwindowMaxSizeRatio : float : subwindowMinSizeRatio
<= subwindowMaxSizeRatio <= 1 (default : 1.)
The maximim size of a subwindow expressed as the ratio of the size
of the original image
subwindowTargetWidth : int > 0 (default : 16)
The width of the subwindows after reinterpolation
subwindowTargetHeight : int > 0 (default : 16)
The height of the subwindows after reinterpolation
subwindowInterpolation : int (default :
SubWindowExtractor.INTERPOLATION_BILINEAR)
The subwindow reinterpolation algorithm. For more information, see
:class:`SubWindowExtractor`
includeOriginalImage : boolean (default : False)
Whether or not to include the original image in the subwindow
extraction process
nbJobs : int >0 or -1 (default : -1)
The number of process to spawn for parallelizing the computation.
If -1, the maximum number is selected. See also :mod:`Joblib`.
verbosity : int >= 0 (default : 10)
The verbosity level
tempFolder : string (directory path) (default : None)
The temporary folder used for memmap. If none, some default folder
will be use (see the :class:`ParallelCoordinator`)
random : bool (default : True)
Whether to use randomness or use a predefined seed
Return
------
coordinator : :class:`Coordinator`
The RandConvCoordinator corresponding to the
set of parameters
Notes
-----
- Filter generator
Base instance of :class:`Finite3SameFilter` with a base instance of
:class:`NumberGenerator` for the values and
:class:`OddUniformGenerator` for the sizes
- Filter size
The filter are square (same width as height)
- Convolver
Base instance of :class:`RGBConvolver`
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformely).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
#RANDOMNESS
swngSeed = None
if random is False:
swngSeed = 0
#CONVOLUTIONAL EXTRACTOR
#Filter generator
#Type/policy parameters, #filters, random
filterPolicyType, filterPolicyParam = filterPolicy
filterGenerator = getFilterGenerator(filterPolicyType, filterPolicyParam,
nbFilters, random)
#Convolver
convolver = RGBConvolver()
#Aggregator
multiPooler = getMultiPoolers(poolings, subwindowTargetHeight,
subwindowTargetWidth)
#SubWindowExtractor
swNumGenerator = NumberGenerator(seed=swngSeed)
swExtractor = SubWindowExtractor(subwindowMinSizeRatio,
subwindowMaxSizeRatio,
subwindowTargetWidth,
subwindowTargetHeight,
subwindowInterpolation, swNumGenerator)
multiSWExtractor = MultiSWExtractor(swExtractor, nbSubwindows, False)
#ConvolutionalExtractor
convolutionalExtractor = ConvolutionalExtractor(filterGenerator,
convolver,
multiSWExtractor,
multiPooler,
includeOriginalImage)
#FEATURE EXTRACTOR
featureExtractor = getFeatureExtractor(extractor[0], extractor[1])
#LOGGER
autoFlush = verbosity >= 40
logger = ProgressLogger(StandardLogger(autoFlush=autoFlush,
verbosity=verbosity))
#COORDINATOR
coordinator = RandConvCoordinator(convolutionalExtractor, featureExtractor,
logger, verbosity)
if nbJobs != 1:
coordinator.parallelize(nbJobs, tempFolder)
return coordinator
def __init__(self, filterGenerator, sparseProb, seed=None):
self._fg = filterGenerator
self._numGen = NumberGenerator(0, 1, seed)
self._prob = sparseProb
#def RoundRobinMFF(filterGenerators, nbFilters, probVect=None):
# if probVect is None:
# ls = [None]*nbFilters
# for i in xrange(nbFilters):
# ls[i] = filterGenerators[i % len(filterGenerators)].next()
# return ls
# else:
# pass # TODO XXX
if __name__ == "__main__":
test = False
if test:
from NumberGenerator import (NumberGenerator, IntegerUniformGenerator,
OddUniformGenerator)
fltGen = FilterGenerator(NumberGenerator(-7, 15),
OddUniformGenerator(30, 16))
fltGen2 = FilterGenerator(IntegerUniformGenerator(15, 20),
OddUniformGenerator(3, 10))
diff3 = Finite3Filter(fltGen, 5)
same3 = Finite3SameFilter(fltGen2, 5)
for f in diff3:
print f[0], f[0].shape
print f[1], f[1].shape
print f[2], f[2].shape
for f in same3:
def __init__(self, filterGenerator, sparseProb, seed=None):
self._fg = filterGenerator
self._numGen = NumberGenerator(0, 1, seed)
self._prob = sparseProb
