#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import swood
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('swood', 'Stochastic Woodland (Depreciated)', 'Basic random forest implimentation (Depreciated)')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(swood.SWood)
doc.addClass(swood.DecTree)
# Copyright 2011 Tom SF Haines
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import dp_al
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'dp_al', 'Dirichlet Process Active Learning',
'Active learning, includes Dirichlet process derived method')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(dp_al.Pool)
doc.addClass(dp_al.Entity)
doc.addClass(dp_al.ConcentrationDP)
#! /usr/bin/env python
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import dpgmm
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('dpgmm', 'Dirichlet Process Gaussian Mixture Model',
'Dynamically resizing Gaussian mixture model')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(dpgmm.DPGMM)
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import df
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('df', 'Decision Forests', 'Extensive random forests implimentation')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(df.DF)
doc.addClass(df.ExemplarSet)
doc.addClass(df.MatrixES)
doc.addClass(df.MatrixGrow)
doc.addClass(df.Goal)
doc.addClass(df.Classification)
doc.addClass(df.DensityGaussian)
doc.addClass(df.Pruner)
doc.addClass(df.PruneCap)
doc.addClass(df.Test)
doc.addClass(df.AxisSplit)
#! /usr/bin/env python
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import p_cat
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'p_cat', 'Probabilistic Classification',
'Standardised interface to probabilistic classifiers with features for active learning'
)
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(p_cat.ProbCat)
doc.addClass(p_cat.ClassifyGaussian)
doc.addClass(p_cat.ClassifyKDE)
doc.addClass(p_cat.ClassifyDPGMM)
doc.addClass(p_cat.ClassifyDF)
#! /usr/bin/env python
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import dhdp
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('dhdp', 'Dual Hierarchical Dirichlet Processes',
'Clustering topic model')
doc.addFile('readme.txt', 'Overview')
# Functions...
doc.addFunction(dhdp.getAlgorithm)
# Classes...
doc.addClass(dhdp.PriorConcDP)
doc.addClass(dhdp.Params)
doc.addClass(dhdp.Document)
doc.addClass(dhdp.Corpus)
doc.addClass(dhdp.DocSample)
doc.addClass(dhdp.Sample)
doc.addClass(dhdp.Model)
doc.addClass(dhdp.DocModel)
#! /usr/bin/env python
# Copyright 2016 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import hg
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('hg', 'Homography',
'Library for constructing and applying homographies.')
doc.addFile('readme.txt', 'Overview')
# Functions...
doc.addFunction(hg.translate)
doc.addFunction(hg.rotate)
doc.addFunction(hg.scale)
doc.addFunction(hg.match)
doc.addFunction(hg.bounds)
doc.addFunction(hg.fit)
doc.addFunction(hg.scaling)
doc.addFunction(hg.fillmasked)
doc.addFunction(hg.transform)
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import video
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('video', 'Video Node System',
'Video processing, a node based system')
doc.addFile('readme.txt', 'Overview')
# Variables...
doc.addVariable(
'MODE_RGB',
'Indicates a connection between nodes that uses a rgb colour stream, for normal video.'
)
doc.addVariable(
'MODE_MASK',
'Indicates a connection between nodes that uses a binary stream, for comunicating masks.'
)
doc.addVariable(
'MODE_FLOW',
'Indicates a connection between nodes that uses a pair of floating point numbers, for communicating optical flow.'
)
# -*- coding: utf-8 -*-
# Copyright 2010 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import svm
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('svm', 'Support Vector Machine',
'Support vector machines, classification only')
doc.addFile('readme.txt', 'Overview')
# Functions...
doc.addFunction(svm.solvePair)
doc.addFunction(svm.looPair)
doc.addFunction(svm.looPairSelect)
# Classes...
doc.addClass(svm.Kernel)
doc.addClass(svm.Params)
doc.addClass(svm.ParamsRange)
doc.addClass(svm.ParamsSet)
doc.addClass(svm.Dataset)
doc.addClass(svm.Model)
doc.addClass(svm.MultiModel)
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import lda
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'lda_var', 'Latent Dirichlet Allocation (variational)',
'Variational implimentation of latent Dirichlet allocation')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(lda.VLDA)
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import lda
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'lda_gibbs', 'Latent Dirichlet Allocation (Gibbs)',
'Gibbs sampling implimentation of latent Dirichlet allocation')
doc.addFile('readme.txt', 'Overview')
# Functions...
doc.addFunction(lda.getAlgorithm)
# Classes...
doc.addClass(lda.Document)
doc.addClass(lda.Topic)
doc.addClass(lda.Corpus)
doc.addClass(lda.Params)
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from viewer import *
from tile_image import *
from tile_mask import *
from tile_value import *
from reticle_overlay import *
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('utils_gui', 'GUI Utilities',
'Library of miscellaneous stuff for doing GUI work.')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(Viewer)
doc.addClass(Layer)
doc.addClass(Viewport)
doc.addClass(TileImage)
doc.addClass(TileMask)
doc.addClass(TileValue)
doc.addClass(ReticleOverlay)
#! /usr/bin/env python
# Copyright 2016 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import ddp
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('ddp', 'Discrete Dynamic Programming',
'Straight forward dynamic programming implimentation.')
doc.addFile('readme.txt', 'Overview')
# Pull in information about the 'types'...
text = []
for name in ddp.DDP.names():
text.append('%s:' % name)
text.append(ddp.DDP.description(name))
text.append('\n')
doc.addOther('\n'.join(text), 'Cost functions', False)
# Classes...
doc.addClass(ddp.DDP)
#! /usr/bin/env python
# Copyright 2016 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
from line_graph import LineGraph
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('line_graph', 'Line Graph', 'Feature heavy representation of a line.')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(LineGraph)
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import gmm
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'gmm', 'Gaussian Mixture Model (plus K-means)',
'Gaussian mixture model, with EM, plus assorted k-means implimentations')
doc.addFile('readme.txt', 'Overview')
# Function that removes the methods that start with 'do' from a class - to hide them in the documentation...
def pruneClassOfDo(cls):
methods = dir(cls)
for method in methods:
if method[:2] == 'do':
delattr(cls, method)
pruneClassOfDo(gmm.KMeansShared)
pruneClassOfDo(gmm.KMeans0)
pruneClassOfDo(gmm.KMeans1)
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import ddhdp
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('ddhdp', 'Delta-Dual Hierarchical Dirichlet Processes',
'Semi-supervised topic model, with clustering')
doc.addFile('readme.txt', 'Overview')
# Functions...
doc.addFunction(ddhdp.getAlgorithm)
# Classes...
doc.addClass(ddhdp.PriorConcDP)
doc.addClass(ddhdp.Params)
doc.addClass(ddhdp.Document)
doc.addClass(ddhdp.Corpus)
doc.addClass(ddhdp.DocSample)
doc.addClass(ddhdp.Sample)
doc.addClass(ddhdp.Model)
doc.addClass(ddhdp.DocModel)
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import smp
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('smp', 'Sparse Multinomial Posterior', 'Estimate a multinomial distribution, given sparse draws')
doc.addFile('readme.txt', 'Overview')
# Variables...
doc.addVariable('smp_code', 'String containing the C++ code that does the actual work for the system.')
# Classes...
doc.addClass(smp.SMP)
doc.addClass(smp.FlagIndexArray)
# Copyright (c) 2011, Tom SF Haines
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import dp_utils
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('dp_utils', 'Dirichlet Process Utilities',
'Utility library for handling Dirichlet processes')
doc.addFile('readme.txt', 'Overview')
# Variables...
doc.addVariable(
'sampling_code',
'Code for sampling from various distributions - uniform, Gaussian, gamma and beta.'
)
doc.addVariable(
'conc_code',
'Contains code to sample a concentration parameter and two classes - one to represent the status of a concentration parameter - its prior and its estimated value, and another to do the same thing for when a concentration parameter is shared between multiple Dirichlet processes.'
)
doc.addVariable(
'dir_est_code',
'Contains a class for doing maximum likelihood estimation of a Dirichlet distrbution given multinomials that have been drawn from it.'
)
#! /usr/bin/env python
# Copyright 2016 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import tps
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'misc', 'Miscellaneous Algorithms',
'Miscellaneous algorithms that ar not big enough to warrent their own module.'
)
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(tps.TPS)
# Copyright 2016 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import frf
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'frf', 'Fast Random Forest',
'Straight random forest implementation, for when you just want a reasonable classifier/regressor.'
)
doc.addFile('readme.txt', 'Overview')
# Pull in information about the 'types'...
text = []
for summary in frf.Forest.summary_list():
text.append(summary['name'])
text.append('code = ' + summary['code'])
text.append(summary['description'])
text.append('')
doc.addOther('\n'.join(text), 'Summary types', False)
text = []
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import rlda
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('rlda', 'Region Latent Dirichlet Allocation',
'Topic model that learns synonyms')
doc.addFile('readme.txt', 'Overview')
# Functions...
doc.addFunction(rlda.getAlgorithm)
# Classes...
doc.addClass(rlda.Document)
doc.addClass(rlda.Corpus)
doc.addClass(rlda.Params)
# -*- coding: utf-8 -*-
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
# This script file generates a kde_inc.html file, which contains all the information needed to use the system.
import kde_inc
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('kde_inc', 'Incrimental kernel density estimation', 'Kernel density estimation with Gaussian kernels and greedy merging beyond a cap')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(kde_inc.PrecisionLOO)
doc.addClass(kde_inc.SubsetPrecisionLOO)
doc.addClass(kde_inc.GMM)
doc.addClass(kde_inc.KDE_INC)
# Copyright 2013 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import ms
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'ms', 'Mean Shift',
'Mean shift, plus kernel density estimation and subspace constrained mean shift.'
)
doc.addFile('readme.txt', 'Overview')
# Pull in information about the supported kernels...
text = []
for kernel in ms.MeanShift.kernels():
text.append(kernel)
text.append('')
d = ms.MeanShift.info(kernel)
text.append(d)
text.append('')
c = ms.MeanShift.info_config(kernel)
if c == None:
#! /usr/bin/env python
# Copyright 2016 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import maxflow
import binary_label
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'graph_cuts', 'Graph Cuts',
'Just max flow and a warpper for solving binary labelling problems.')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(maxflow.MaxFlow)
doc.addClass(binary_label.BinaryLabel)
#! /usr/bin/env python
# Copyright 2014 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import gbp
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen('gbp', 'Gaussian belief propagation', 'Linear equation solver that is conveniant in certain situations')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(gbp.GBP)
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2011 Tom SF Haines
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import gcp
from utils import doc_gen
# Setup...
doc = doc_gen.DocGen(
'gcp', 'Gaussian Conjugate Prior',
'Library of distributions focused on the Gaussian and its conjugate prior')
doc.addFile('readme.txt', 'Overview')
# Classes...
doc.addClass(gcp.Gaussian)
doc.addClass(gcp.GaussianInc)
doc.addClass(gcp.Wishart)
doc.addClass(gcp.StudentT)
doc.addClass(gcp.GaussianPrior)