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Copyright.txt

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The Accord.NET Framework
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Copyright (c) 2009-2015, Accord.NET Authors <authors @ accord-framework.net>
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Copyright (c) 2009-2016, Accord.NET Authors <authors @ accord-framework.net>
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This library is free software; you can redistribute it and/or modify it under the terms of
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the GNU Lesser General Public License as published by the Free Software Foundation; either
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Externals/ffmpeg/.gitignore

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!bin

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License.txt

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The Accord.NET Framework
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Copyright (c) 2009-2015, Accord.NET Authors <authors @ accord-framework.net>
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This library is free software; you can redistribute it and/or modify it under the terms of
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the GNU Lesser General Public License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
8-
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The copyright holders provide no reassurances that the source code provided does
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not infringe any patent, copyright, or any other intellectual property rights of
11-
third parties. The copyright holders disclaim any liability to any recipient for
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claims brought against recipient by any third party for infringement of that parties
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intellectual property rights.
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This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
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without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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See the GNU Lesser General Public License for more details.
18-
19-
Warning: Depending on your installation options, some modules of the library may be available
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under different licenses. Those modules have been identified as GPL or noncommercial only. In
21-
this case, check the copyright headers of each extra module for more details before installing
22-
or using them.
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Copyright (c) 2009-2016, Accord.NET Authors <[email protected]>
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5+
This library is free software; you can redistribute it and/or modify it
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under the terms of the GNU Lesser General Public License as published by
7+
the Free Software Foundation; either version 2.1 of the License, or (at
8+
your option) any later version.
9+
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The copyright holders provide no reassurances that the source code provided
11+
does not infringe any patent, copyright, or any other intellectual property
12+
rights of third parties. The copyright holders disclaim any liability to
13+
any recipient for claims brought against recipient by any third party for
14+
infringement of that parties intellectual property rights.
15+
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This library is distributed in the hope that it will be useful, but WITHOUT
17+
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
18+
FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License
19+
for more details.
20+
21+
Warning: Depending on your installation options, some modules of the library
22+
may be available under different licenses. Those modules have been identified
23+
as GPL or noncommercial only. In this case, check the copyright headers of each
24+
extra module for more details before installing or using them.
2325

24-
The integral text of the GNU Lesser General Public License is reproduced below. Installing
25-
or using this software means you have understand and agreed by the terms of the license. A
26-
official version of the license is also available at <http://www.gnu.org/licenses/>.
26+
The integral text of the GNU Lesser General Public License is reproduced
27+
below. Installing or using this software means you have understand and agreed
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by the terms of the license. A official version of the license is also
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available at <http://www.gnu.org/licenses/>.
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README.txt README.md

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# Build the framework solution using Mono
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mdtool build -c:"NET40" Sources/Accord.NET.Mono.sln
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Join the chat at https://gitter.im/accord-net/framework

Release notes.txt

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Accord.NET Framework 3.2.0 release notes
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-----------------------------------------
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20.08.2016.
4+
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Accord.NET 3.2 "auto-generated" release
6+
7+
Version updates and fixes:
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- GH-76/GC-24: Add easier creating and handling of factors for categorical variables
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- GH-123: Bug in the Euclidean on Accord.Math.Distance
10+
- GH-124: Fixing the Envelop filter as missing loop variables were not being incremented
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- GH-135: When the from and to ranges are equal, scaled values should remain unchanged
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- GH-159: Gamma Distribution Fit stalls for some arrays
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- GH-162: ntdll on OS X
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- GH-167: Posterior method has wrong signature in continuous hidden Markov Models
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- GH-171: Quadratic Programming (Goldfarb-Idnani) NoPossibleSolution on possible problems
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- GH-188: ProbabilisticOutputCalibration Class Example Incorrect Object Name
17+
- GH-206: Chessboard distance is incorrect
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- GH-214: Bug found in ReplaceChannel filter
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- GH-215: Bug Found in DecisionTrees.Learning.ID3Learning.
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- GH-225: Independent Component Analysis not converging
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- GH-232: Bug in Levenshtein distance.
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- GH-234: The subset of observations corresponding to a decision node may contains duplicates
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- GH-235: The getMaxChild method returns the max grandchild
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- GH-236: Possibly-biased comparison between errors
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- GH-237: The subset of observations corresponding to a decision node may contains duplicates
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- GH-240: Re() and Im() function of ComplexMatrix generates a OutOfRangeException
27+
28+
* General
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- In this release, the Matrix library from Accord.Math has been almost completely
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redesigned to make heavy use of automatic code generation. This results in more
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code reuse, more consistent interfaces and the availability of many methods which
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before were available only for Double to almost all native numerical types in the
33+
.NET Framework;
34+
- The framework now contains core classes and interfaces for defining classification
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and regression models and their respective learning algorithms, offering a more
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standard interface when using different parts of the framework;
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- The framework now offers a Accord.Serializer class that should be responsible for
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serializing and deserializing any object from the framework, and will take care of
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versioning in case of breaking changes between releases;
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- All AForge.NET namespaces have been finally moved to inside Accord.NET, although
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some functionality is still duplicate.
42+
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* Core
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- Adding Interlocked operations (Increment, Add) for double values;
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- To<> universal converter can now convert jagged arrays;
46+
- Adding a common framework to unify all classification models, and all learning algorithms;
47+
- Integrating the AForge.NET Range classes in the framework, adding ByteRange;
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- Adding a common serialization mechanism to the framework to manage backwards compatibility;
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- All classes from Accord.MachineLearning.Structures have been moved into Accord.Collections;
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- Updating RedBlackTrees to implement the new base classes for tree structures;
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- Updating KD-Trees to implement the base classes for tree structures (introduces breaking changes).
52+
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* Sample applications
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- Fixing wrong arguments in sample applications.
55+
56+
* Math
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- Revamped matrix library making heavy use of code generation with T4 templates;
58+
- Matrix dot products, and elementwise operations are now auto-generated;
59+
- Renaming InnerProduct to Dot, and marking previous products as obsolete;
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- Vector Range, Scale and Interval are now auto-generated;
61+
- Standardizing the way Vectors, Matrices and Jagged matrices are created and handled
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in the framework;
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- Adding OneHot and KHot methods overloads for creating vectors using boolean masks;
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- Adding ArgMin and ArgMax methods to Vector, Jagged and Multidimensional matrices;
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- Re-implementing Matrix.Sum and Matrix.Product using T4 templates;
66+
- Breaking change: Sum() now computes the Sum over the entire matrix (before it needed
67+
to be done with Sum().Sum(). In order to compute the sum vector over rows, use matrix.Sum(0)
68+
and for columns, matrix.Sum(1);
69+
- Chessboard distance has been removed as it is the same as Chebyshev;
70+
- Moving AForge.NET's old Random classes into the framework, and marking them as deprecated;
71+
- Adding a log1pexp method for computing (1.0 + Math.Exp(-sum)) without loss of precision;
72+
- Adding new random generators based on Marsaglia's Ziggurat method;
73+
- Introducing a new, generic IRandomNumberGenerator interface so existing statistical
74+
distributions can be used as Random Number Generators;
75+
- Updating Matrix.IsEqual method to use the auto-generated overloads if possible;
76+
- Replacing the previous framework-wide generator with a better API;
77+
- Improving the framework-wide random number generator so generators created in short
78+
timespans do not get initialized with the same seed: Now, updating a seed will not
79+
affect existent random generators in other threads. It will affect only newly created
80+
generators and the one in the current thread;
81+
- Fixing the DiagonalMatrix property in SingularValueDecomposition and
82+
JaggedSingularValueDecomposition so the returned diagonal matrices has the necessary
83+
dimensions to reconstruct the original matrix using the decomposition main formulation;
84+
- Fixing a bug in Combinatorics.Sequences method where the current vector would be returned
85+
instead of a copy when inPlace = false;
86+
- Distance functions can now be auto-generated from classes from the framework;
87+
- Adding Dice, Jaccard, Kulczynski, Matching, Rogers-Tanimoto, Russel-Rao, Sokal Michener,
88+
Sokal Sneath, Yule, Bhattacharyya and LogLikelihood distances as proper classes;
89+
- Updating IsEqual to support absoluete and relative tolarance thresholds;
90+
- Adding a Histogram method for creating a histogram from an array of integer values;
91+
- Updating the Interval, Range and Scale method overloads to be automatically generated;
92+
- Adding loss functions to be used in the unified framework;
93+
- Moving the Elementwise class to a separate Accord.Math.Core project in order to avoid
94+
excessive build times due the number of auto-generated methods in this class;
95+
- Adding overloads to Eigenvalue decomposition to automatically sorter eigenvectors and
96+
eigenvalues in descending order of absolute eigenvalue;
97+
- Adding a dedicated Sort static class with ordering-related methods such as Partition,
98+
Introsort and NthElement.
99+
- Expanding decompositions with two additional methods: GetInformationMatrix and Reverse
100+
GetInformationMatrix can be used to retrieve the standard errors for each coefficient
101+
when solving a linear system; Reverse reconstructs the original matrix using the definition
102+
of the decomposition;
103+
- Deprecating Submatrix in favor of Get (methods with non-inclusive last indices);
104+
- Adding ArgSort function for retrieving the indices that can be used to sort a vector;
105+
- Adding LogSumExp to the set of special functions.
106+
107+
* MachineLearning
108+
- Adding a base foundation to encompass all classification and regression models in the
109+
framework as well as their learning algorithms: common interfaces and base classes for
110+
classifiers, distance-based classifiers and generative classifiers; common interfaces
111+
and base classes for supervised and unsupervised learning algorithms;
112+
- Updating Support Vector Machines, Decision Trees, Naive Bayes, Regressions and Analyses
113+
to use the new classes;
114+
- Unifying Linear and Kernel SupportVectorMachines, updating their classes to accept the
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Kernel function as a generic parameter: when the kernel function is a ValueType, this
116+
forces generic classes to be compiled specifically for each kernel type, allowing for
117+
the inlining of the kernel function calls;
118+
- Updating the way compact SVMs are represented: instead of having only a weight vector
119+
and no support vectors, compact machines have a single support vector and a single weight
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of value one, eliminating what before was a special case;
121+
- Adding classes for OneVsOne and OneVsRest classifiers, separating the functionality that
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was previously inside MulticlassSupportVectorMachine and MultilabelSupportVectorMachine;
123+
- Fixing multiple issues with ErrorBasedPruning (YaronK);
124+
- Updating GridSearch to implement ToString methods for easier debugging;
125+
- Updating Linear machines and learning algorithms to accept sparse kernels;
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- Deprecating the previous sparse vector implementations and moving the current implementation
127+
to the existing Linear class, since they represent the same operation;
128+
- Adding a true implementation for LibSVM-style Sparse vectors;
129+
- Updating SparseReader to read sparse vectors using the new Sparse representation;
130+
- Refactoring the clustering namespace to increase code reuse between the different algorithms;
131+
- Updating K-Means, GMM and BagOfWords to expose a ParallelOptions object that can
132+
be used to configure and stop the parallelization of those algorithms;
133+
- Updating K-Means to support sample weights;
134+
- Correcting multiple random initializations of Gaussian mixture model;
135+
- Adding a PriorityQueue class based on the MIT-licensed code by Daniel "BlueRaja" Pflughoeft.
136+
- Adding Vantage-Point and Space-Partitioning trees and Barnes Hutt t-SNE based on the original
137+
code from Laurens van der Maaten BH t-SNE implementation;
138+
- Adding a basic implementation for the Apriori algorithm.
139+
140+
* Imaging
141+
- Updating static methods in AForge.NET's Image class to become extension methods;
142+
- Implementing ICloneable in all corner and feature detectors.
143+
144+
* Neuro
145+
- Updating ResilientBackpropagation with the improvements from iRProp+.
146+
147+
* Statistics
148+
- Adding Non negative Least Squares regression;
149+
- Adding Procrustes Analysis;
150+
- Deprecating IAnalysis in favor of the new framelet for classification,
151+
regression and transformation methods;
152+
- Merging AForge.NET and Accord.NET Histogram classes;
153+
- Updating IFittingOptions to implement ICloneable;
154+
- Adding constructors to Independent distributions accepting a lambda function
155+
to initialize inner components instead of relying on cloning;
156+
- Adding a Classes class to provide methods that operate with categorical/label data,
157+
such as converting boolean, double or integer values to [0;1] or [-1; +1] indicators;
158+
- Adding Decide methods to unambiguously transform a distance/score value into a boolean;
159+
- Updating statistic distributions to implement the IRandomNumberGenerator interface, meaning
160+
any distribution can now be used as random number generator;
161+
- Adding the Metropolis-Hasting sampler to generate samples from multivariate distributions
162+
that do not have specialized samplers;
163+
- Adding named constructors for building regressions directly from coefficient vectors;
164+
- Updating kernels to rely in Accord.Math.IDistance instead of the previous IDistance from
165+
the Statistics namespace;
166+
- Adding Pearson's Universal Kernel, Thin Spline Plate and Hellinger kernels
167+
contributed by Diego Catalano;
168+
- Moving standard statistical measures (i.e. mean, standard deviation, variance, ...) to a
169+
separate Measures class;
170+
- Updating Mean methods to operate in the same way as Sum: if a dimension is not specified,
171+
the Mean will be computed across all dimensions of the matrix;
172+
- Updating Hidden Markov Models to use the new Tagger interfaces and base classes.
173+
174+
* Genetics
175+
- Updating the Genetics project to use the new sample generators based on statistical
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distributions;
177+
178+
179+
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Accord.NET Framework 3.0.0 release notes
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-----------------------------------------
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16.08.2015.
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- Adding support for inclusive upper bounds in the Histogram class;
12181398
- Adding more selection rules for the number and widths of Histogram bins;
12191399
- Fixing Histogram computing when data is constant (Origo Issue #23);
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- Renaming weighted sample methods from Statistics.Tools.
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- Renaming weighted sample methods from Measures.
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* Accord.Imaging
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- Adding Speeded-Up Robust Features (SURF) detector and descriptor;

Samples/Audio/Fourier (FFT)/MainForm.cs

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// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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//
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using Accord;
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using Accord.Audio;
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using Accord.Audio.Windows;
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using Accord.DirectSound;
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using AForge;
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using AForge.Math;
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using Accord.Math;
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using System;
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using System.Drawing;
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using System.Numerics;

Samples/Audio/Recorder/MainForm.Designer.cs

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Samples/Fuzzy/Robot (Inference)/MainForm.cs

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using System.Data;
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using System.Threading;
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using System.Diagnostics;
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using AForge.Fuzzy;
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using Accord.Fuzzy;
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namespace SampleApp
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{
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pbRobot.Left = pbRobot.Left + IncX;
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}
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// Starting and stopping the AGV's moviment a
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// Starting and stopping the AGV's movement a
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private void btnRun_Click(object sender, System.EventArgs e)
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{
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Button b = (sender as Button);
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// One step of the AGV
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private void AGVStep()
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{
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if (FirstInference) GetMeasures();
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if (FirstInference)
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GetMeasures();
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try
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{

Samples/Fuzzy/Sets (Fuzzy, Linguistic)/MainForm.Designer.cs

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Samples/Fuzzy/Sets (Fuzzy, Linguistic)/MainForm.cs

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using System.Text;
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using System.Windows.Forms;
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using AForge.Fuzzy;
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using AForge.Controls;
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using AForge;
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using Accord.Fuzzy;
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using Accord.Controls;
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using Accord;
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{

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