コード例 #1
0
        public void RunTest()
        {
            var dataset = SequentialMinimalOptimizationTest.yinyang;

            double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
            int[] labels = dataset.GetColumn(2).ToInt32();

            Accord.Math.Random.Generator.Seed = 0;

            var svm = new SupportVectorMachine(inputs: 2);
            var teacher = new ProbabilisticDualCoordinateDescent(svm, inputs, labels);

            teacher.Tolerance = 1e-10;
            teacher.UseComplexityHeuristic = true;

            Assert.IsFalse(svm.IsProbabilistic);
            double error = teacher.Run();
            Assert.IsTrue(svm.IsProbabilistic);

            double[] weights = svm.ToWeights();

            Assert.AreEqual(0.13, error);
            Assert.AreEqual(3, weights.Length);
            Assert.AreEqual(-0.52913278486359605, weights[0], 1e-4);
            Assert.AreEqual(-1.6426069611746976, weights[1], 1e-4);
            Assert.AreEqual(-0.77766953652287762, weights[2], 1e-4);

            Assert.AreEqual(svm.Threshold, weights[0]);
        }
コード例 #2
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ファイル: SVM.cs プロジェクト: abaffa/INF1771
        public void Run()
        {
            // Example AND problem
            double[][] inputs =
            {
            new double[] { 0, 0 }, // 0 and 0: 0 (label -1)
            new double[] { 0, 1 }, // 0 and 1: 0 (label -1)
            new double[] { 1, 0 }, // 1 and 0: 0 (label -1)
            new double[] { 1, 1 }  // 1 and 1: 1 (label +1)
            };

            // Dichotomy SVM outputs should be given as [-1;+1]
            int[] labels =
            {
            // 0,  0,  0, 1
              -1, -1, -1, 1
            };

            // Create a Support Vector Machine for the given inputs
            SupportVectorMachine machine = new SupportVectorMachine(inputs[0].Length);

            // Instantiate a new learning algorithm for SVMs
            SequentialMinimalOptimization smo = new SequentialMinimalOptimization(machine, inputs, labels);

            // Set up the learning algorithm
            smo.Complexity = 1.0;

            // Run the learning algorithm
            double error = smo.Run();

            // Compute the decision output for one of the input vectors
            int decision = System.Math.Sign(machine.Compute(inputs[0]));
        }
コード例 #3
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        public void RunTest()
        {
            double[][] input =
            {
                new double[] { 55, 0 }, // 0 - no cancer
                new double[] { 28, 0 }, // 0
                new double[] { 65, 1 }, // 0
                new double[] { 46, 0 }, // 1 - have cancer
                new double[] { 86, 1 }, // 1
                new double[] { 56, 1 }, // 1
                new double[] { 85, 0 }, // 0
                new double[] { 33, 0 }, // 0
                new double[] { 21, 1 }, // 0
                new double[] { 42, 1 }, // 1
            };

            double[] output =
            {
                0, 0, 0, 1, 1, 1, 0, 0, 0, 1
            };

            int[] labels = output.Apply(x => x > 0 ? +1 : -1);

            var svm = new SupportVectorMachine(inputs: 2);
            var teacher = new ProbabilisticNewtonMethod(svm, input, labels);

            teacher.Tolerance = 1e-10;
            teacher.Complexity = 1e+10;

            Assert.IsFalse(svm.IsProbabilistic);
            double error = teacher.Run();
            Assert.IsTrue(svm.IsProbabilistic);
            Assert.AreEqual(0.2, error);

            Assert.AreEqual(0.02064511826338301, svm.SupportVectors[0][0]);
            Assert.AreEqual(1.767889310996118, svm.SupportVectors[0][1]);
            Assert.AreEqual(-2.4577464317497455, svm.Threshold);

            var regression = LogisticRegression.FromWeights(svm.ToWeights());

            double[] actual = new double[output.Length];
            for (int i = 0; i < actual.Length; i++)
                actual[i] = regression.Compute(input[i]);

            double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701
            double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331
            

            Assert.AreEqual(1.0208597028836701, ageOdds, 1e-4);
            Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-4);

            Assert.IsFalse(Double.IsNaN(ageOdds));
            Assert.IsFalse(Double.IsNaN(smokeOdds));

            Assert.AreEqual(-2.4577464307294092, regression.Intercept, 1e-8);
            Assert.AreEqual(-2.4577464307294092, regression.Coefficients[0], 1e-8);
            Assert.AreEqual(0.020645118265359252, regression.Coefficients[1], 1e-8);
            Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
        }
コード例 #4
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 public void Setup()
 {
     ksvm = new SupportVectorMachine<Polynomial>(inputs: 2, kernel: new Polynomial(2));
     smo = new SequentialMinimalOptimization<Polynomial>()
     {
         Model = ksvm
     };
 }
コード例 #5
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 public SupportVectorMachine<Polynomial> v3_1_0()
 {
     ksvm = new SupportVectorMachine<Polynomial>(inputs: 2, kernel: new Polynomial(2));
     smo = new SequentialMinimalOptimization<Polynomial>()
     {
         Model = ksvm
     };
     smo.Learn(inputs, outputs);
     return ksvm;
 }
コード例 #6
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ファイル: Program.cs プロジェクト: CanerPatir/framework
        private static void and()
        {
            // Create a simple binary AND
            // classification problem:

            double[][] problem =
            {
                //             a    b    a + b
                new double[] { 0,   0,     0    },
                new double[] { 0,   1,     0    },
                new double[] { 1,   0,     0    },
                new double[] { 1,   1,     1    },
            };

            // Get the two first columns as the problem
            // inputs and the last column as the output
            
            // input columns
            double[][] inputs = problem.GetColumns(0, 1);

            // output column
            int[] outputs = problem.GetColumn(2).ToInt32();

            // Plot the problem on screen
            ScatterplotBox.Show("AND", inputs, outputs).Hold();


            // However, SVMs expect the output value to be
            // either -1 or +1. As such, we have to convert
            // it so the vector contains { -1, -1, -1, +1 }:
            //
            outputs = outputs.Apply(x => x == 0 ? -1 : 1);


            // Create a new linear-SVM for two inputs (a and b)
            SupportVectorMachine svm = new SupportVectorMachine(inputs: 2);

            // Create a L2-regularized L2-loss support vector classification
            var teacher = new LinearDualCoordinateDescent(svm, inputs, outputs)
            {
                Loss = Loss.L2,
                Complexity = 1000,
                Tolerance = 1e-5
            };

            // Learn the machine
            double error = teacher.Run(computeError: true);

            // Compute the machine's answers for the learned inputs
            int[] answers = inputs.Apply(x => Math.Sign(svm.Compute(x)));

            // Plot the results
            ScatterplotBox.Show("SVM's answer", inputs, answers).Hold();
        }
コード例 #7
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        public void RunTest()
        {
            Accord.Math.Tools.SetupGenerator(0);

            // Example regression problem. Suppose we are trying
            // to model the following equation: f(x, y) = 2x + y

            double[][] inputs = // (x, y)
            {
                new double[] { 0,  1 }, // 2*0 + 1 =  1
                new double[] { 4,  3 }, // 2*4 + 3 = 11
                new double[] { 8, -8 }, // 2*8 - 8 =  8
                new double[] { 2,  2 }, // 2*2 + 2 =  6
                new double[] { 6,  1 }, // 2*6 + 1 = 13
                new double[] { 5,  4 }, // 2*5 + 4 = 14
                new double[] { 9,  1 }, // 2*9 + 1 = 19
                new double[] { 1,  6 }, // 2*1 + 6 =  8
            };

            double[] outputs = // f(x, y)
            {
                    1, 11, 8, 6, 13, 14, 19, 8
            };

            // Create a new linear Support Vector Machine 
            var machine = new SupportVectorMachine(inputs: 2);

            // Create the linear regression coordinate descent teacher
            var learn = new LinearRegressionCoordinateDescent(machine, inputs, outputs)
            {
                Complexity = 10000000,
                Epsilon = 1e-10
            };

            // Run the learning algorithm
            double error = learn.Run();

            // Compute the answer for one particular example
            double fxy = machine.Compute(inputs[0]); // 1.000

            // Check for correct answers
            double[] answers = new double[inputs.Length];
            for (int i = 0; i < answers.Length; i++)
                answers[i] = machine.Compute(inputs[i]);

            Assert.AreEqual(1.0, fxy, 1e-5);
            for (int i = 0; i < outputs.Length; i++)
                Assert.AreEqual(outputs[i], answers[i], 1e-2);
        }
コード例 #8
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        public void LearnTest()
        {

            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] xor =
            {
                -1,
                 1,
                 1,
                -1
            };

            var kernel = new Polynomial(2, 0.0);

            double[][] augmented = new double[inputs.Length][];
            for (int i = 0; i < inputs.Length; i++)
                augmented[i] = kernel.Transform(inputs[i]);

            SupportVectorMachine machine = new SupportVectorMachine(augmented[0].Length);

            // Create the Least Squares Support Vector Machine teacher
            var learn = new LinearDualCoordinateDescent(machine, augmented, xor);

            // Run the learning algorithm
            double error = learn.Run();

            Assert.AreEqual(0, error);

            int[] output = augmented.Apply(p => Math.Sign(machine.Compute(p)));
            for (int i = 0; i < output.Length; i++)
                Assert.AreEqual(System.Math.Sign(xor[i]), System.Math.Sign(output[i]));
        }
コード例 #9
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        public void ComplexityHeuristicTest()
        {
            var dataset = yinyang;

            double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
            int[] labels = dataset.GetColumn(2).ToInt32();

            var linear = new SupportVectorMachine(inputs[0].Length);

            Linear kernel = new Linear(0);
            var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);

            var smo1 = new SequentialMinimalOptimization(machine, inputs, labels);
            smo1.UseClassProportions = true;
            smo1.UseComplexityHeuristic = true;
            double e1 = smo1.Run();

            var smo2 = new SequentialMinimalOptimization(linear, inputs, labels);
            smo2.UseClassProportions = true;
            smo2.UseComplexityHeuristic = true;
            double e2 = smo2.Run();

            Assert.AreEqual(smo1.Complexity, smo2.Complexity);
            Assert.AreEqual(e1, e2);
        }
コード例 #10
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ファイル: Program.cs プロジェクト: accord-net/framework
        private static void linearSvm(double[][] inputs, int[] outputs)
        {
            // Create a linear binary machine with 2 inputs
            var svm = new SupportVectorMachine(inputs: 2);

            // Create a L2-regularized L2-loss optimization algorithm for
            // the dual form of the learning problem. This is *exactly* the
            // same method used by LIBLINEAR when specifying -s 1 in the 
            // command line (i.e. L2R_L2LOSS_SVC_DUAL).
            //
            var teacher = new LinearCoordinateDescent(svm, inputs, outputs);

            // Teach the vector machine
            double error = teacher.Run();

            // Classify the samples using the model
            int[] answers = inputs.Apply(svm.Compute).Apply(System.Math.Sign);

            // Plot the results
            ScatterplotBox.Show("Expected results", inputs, outputs);
            ScatterplotBox.Show("LinearSVM results", inputs, answers);

            // Grab the index of multipliers higher than 0
            int[] idx = teacher.Lagrange.Find(x => x > 0);

            // Select the input vectors for those
            double[][] sv = inputs.Submatrix(idx);

            // Plot the support vectors selected by the machine
            ScatterplotBox.Show("Support vectors", sv).Hold();
        }
コード例 #11
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        public void ComputeTest5()
        {
            var dataset = SequentialMinimalOptimizationTest.yinyang;
            var inputs = dataset.Submatrix(null, 0, 1).ToArray();
            var labels = dataset.GetColumn(2).ToInt32();

            var kernel = new Polynomial(2, 0);

            {
                var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
                var smo = new SequentialMinimalOptimization(machine, inputs, labels);
                smo.UseComplexityHeuristic = true;

                double error = smo.Run();
                Assert.AreEqual(0.2, error);

                Assert.AreEqual(0.11714451552090824, smo.Complexity);

                int[] actual = new int[labels.Length];
                for (int i = 0; i < actual.Length; i++)
                    actual[i] = Math.Sign(machine.Compute(inputs[i]));

                ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
                Assert.AreEqual(20, matrix.FalseNegatives);
                Assert.AreEqual(0, matrix.FalsePositives);
                Assert.AreEqual(30, matrix.TruePositives);
                Assert.AreEqual(50, matrix.TrueNegatives);
            }

            {
                Accord.Math.Tools.SetupGenerator(0);

                var projection = inputs.Apply(kernel.Transform);
                var machine = new SupportVectorMachine(projection[0].Length);
                var smo = new LinearNewtonMethod(machine, projection, labels);
                smo.UseComplexityHeuristic = true;

                double error = smo.Run();
                Assert.AreEqual(0.18, error);

                Assert.AreEqual(0.11714451552090821, smo.Complexity, 1e-15);

                int[] actual = new int[labels.Length];
                for (int i = 0; i < actual.Length; i++)
                    actual[i] = Math.Sign(machine.Compute(projection[i]));

                ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
                Assert.AreEqual(17, matrix.FalseNegatives);
                Assert.AreEqual(1, matrix.FalsePositives);
                Assert.AreEqual(33, matrix.TruePositives);
                Assert.AreEqual(49, matrix.TrueNegatives);
            }

        }
コード例 #12
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ファイル: Program.cs プロジェクト: CanerPatir/framework
        private static void cancer()
        {
            // Create a new LibSVM sparse format data reader
            // to read the Wisconsin's Breast Cancer dataset
            //
            var reader = new SparseReader("examples-sparse.txt");

            int[] outputs; // Read the classification problem into dense memory
            double[][] inputs = reader.ReadToEnd(sparse: false, labels: out outputs);

            // The dataset has output labels as 4 and 2. We have to convert them
            // into negative and positive labels so they can be properly processed.
            //
            outputs = outputs.Apply(x => x == 2 ? -1 : +1);

            // Create a new linear-SVM for the problem dimensions
            var svm = new SupportVectorMachine(inputs: reader.Dimensions);

            // Create a learning algorithm for the problem's dimensions
            var teacher = new LinearDualCoordinateDescent(svm, inputs, outputs)
            {
                Loss = Loss.L2,
                Complexity = 1000,
                Tolerance = 1e-5
            };

            // Learn the classification
            double error = teacher.Run();

            // Compute the machine's answers for the learned inputs
            int[] answers = inputs.Apply(x => Math.Sign(svm.Compute(x)));

            // Create a confusion matrix to show the machine's performance
            var m = new ConfusionMatrix(predicted: answers, expected: outputs);

            // Show it onscreen
            DataGridBox.Show(new ConfusionMatrixView(m));
        }
コード例 #13
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        public void ReceiverOperatingCharacteristicConstructorTest3()
        {
            // This example shows how to measure the accuracy of a 
            // binary classifier using a ROC curve. For this example,
            // we will be creating a Support Vector Machine trained
            // on the following instances:

            double[][] inputs =
            {
                // Those are from class -1
                new double[] { 2, 4, 0 },
                new double[] { 5, 5, 1 },
                new double[] { 4, 5, 0 },
                new double[] { 2, 5, 5 },
                new double[] { 4, 5, 1 },
                new double[] { 4, 5, 0 },
                new double[] { 6, 2, 0 },
                new double[] { 4, 1, 0 },

                // Those are from class +1
                new double[] { 1, 4, 5 },
                new double[] { 7, 5, 1 },
                new double[] { 2, 6, 0 },
                new double[] { 7, 4, 7 },
                new double[] { 4, 5, 0 },
                new double[] { 6, 2, 9 },
                new double[] { 4, 1, 6 },
                new double[] { 7, 2, 9 },
            };

            int[] outputs =
            {
                -1, -1, -1, -1, -1, -1, -1, -1, // fist eight from class -1
                +1, +1, +1, +1, +1, +1, +1, +1  // last eight from class +1
            };

            // Create a linear Support Vector Machine with 4 inputs
            SupportVectorMachine machine = new SupportVectorMachine(inputs: 3);

            // Create the sequential minimal optimization teacher
            SequentialMinimalOptimization learn = new SequentialMinimalOptimization(machine, inputs, outputs);

            // Run the learning algorithm
            double error = learn.Run();

            // Extract the input labels predicted by the machine
            double[] predicted = new double[inputs.Length];
            for (int i = 0; i < predicted.Length; i++)
                predicted[i] = machine.Compute(inputs[i]);


            // Create a new ROC curve to assess the performance of the model
            var roc = new ReceiverOperatingCharacteristic(outputs, predicted);

            roc.Compute(100); // Compute a ROC curve with 100 points
            /*
                        // Generate a connected scatter plot for the ROC curve and show it on-screen
                        ScatterplotBox.Show(roc.GetScatterplot(includeRandom: true), nonBlocking: true)

                            .SetSymbolSize(0)      // do not display data points
                            .SetLinesVisible(true) // show lines connecting points
                            .SetScaleTight(true)   // tighten the scale to points
                            .WaitForClose();
            */

            Assert.AreEqual(0.7890625, roc.Area);
            // Assert.AreEqual(0.1174774, roc.StandardError, 1e-6); HanleyMcNeil
            Assert.AreEqual(0.11958120746409709, roc.StandardError, 1e-6);
        }
コード例 #14
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ファイル: Program.cs プロジェクト: CanerPatir/framework
        private static void xor()
        {
            // Create a simple binary XOR
            // classification problem:

            double[][] problem =
            {
                //             a    b    a XOR b
                new double[] { 0,   0,      0    },
                new double[] { 0,   1,      1    },
                new double[] { 1,   0,      1    },
                new double[] { 1,   1,      0    },
            };

            // Get the two first columns as the problem
            // inputs and the last column as the output

            // input columns
            double[][] inputs = problem.GetColumns(0, 1);

            // output column
            int[] outputs = problem.GetColumn(2).ToInt32();

            // Plot the problem on screen
            ScatterplotBox.Show("XOR", inputs, outputs).Hold();


            // However, SVMs expect the output value to be
            // either -1 or +1. As such, we have to convert
            // it so the vector contains { -1, -1, -1, +1 }:
            //
            outputs = outputs.Apply(x => x == 0 ? -1 : 1);


            // Create a new linear-SVM for two inputs (a and b)
            SupportVectorMachine svm = new SupportVectorMachine(inputs: 2);

            // Create a L2-regularized L2-loss support vector classification
            var teacher = new LinearDualCoordinateDescent(svm, inputs, outputs)
            {
                Loss = Loss.L2,
                Complexity = 1000,
                Tolerance = 1e-5
            };

            // Learn the machine
            double error = teacher.Run(computeError: true);

            // Compute the machine's answers for the learned inputs
            int[] answers = inputs.Apply(x => Math.Sign(svm.Compute(x)));

            // Plot the results
            ScatterplotBox.Show("SVM's answer", inputs, answers).Hold();

            // Use an explicit kernel expansion to transform the 
            // non-linear classification problem into a linear one
            //
            // Create a quadratic kernel
            Quadratic quadratic = new Quadratic(constant: 1);
            
            // Project the inptus into a higher dimensionality space
            double[][] expansion = inputs.Apply(quadratic.Transform);


            
            // Create a new linear-SVM for the transformed input space
            svm = new SupportVectorMachine(inputs: expansion[0].Length);

            // Create the same learning algorithm in the expanded input space
            teacher = new LinearDualCoordinateDescent(svm, expansion, outputs)
            {
                Loss = Loss.L2,
                Complexity = 1000,
                Tolerance = 1e-5
            };

            // Learn the machine
            error = teacher.Run(computeError: true); 

            // Compute the machine's answers for the learned inputs
            answers = expansion.Apply(x => Math.Sign(svm.Compute(x)));

            // Plot the results
            ScatterplotBox.Show("SVM's answer", inputs, answers).Hold();
        }
コード例 #15
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        public void SequentialMinimalOptimizationConstructorTest()
        {
            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] or =
            {
                0,
                0,
                0,
                +1
            };

            // Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
            SupportVectorMachine machine = new SupportVectorMachine(inputs[0].Length);

            bool thrown = false;
            try
            {
                SequentialMinimalOptimization learn = new SequentialMinimalOptimization(machine, inputs, or);
            }
            catch (ArgumentOutOfRangeException)
            {
                thrown = true;
            }

            Assert.IsTrue(thrown);
        }
コード例 #16
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        public void RunTest2()
        {
            var dataset = SequentialMinimalOptimizationTest.yinyang;

            double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
            int[] labels = dataset.GetColumn(2).ToInt32();

            var svm = new SupportVectorMachine(inputs: 2);
            var teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels);

            teacher.Tolerance = 1e-10;
            teacher.Complexity = 1e+10;

            double error = teacher.Run();

            double[] weights = svm.ToWeights();

            Assert.AreEqual(0.12, error);
            Assert.AreEqual(3, weights.Length);
            Assert.AreEqual(-1.3231203367770932, weights[0]);
            Assert.AreEqual(-3.0227742288788493, weights[1]);
            Assert.AreEqual(-0.73074823290553259, weights[2]);

            Assert.AreEqual(svm.Threshold, weights[0]);
        }
コード例 #17
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ファイル: MainForm.cs プロジェクト: accord-net/framework
        /// <summary>
        ///   Creates a Support Vector Machine and teaches it to recognize
        ///   the previously loaded dataset using the current UI settings.
        /// </summary>
        /// 
        private void btnCreate_Click(object sender, EventArgs e)
        {
            if (dgvLearningSource.DataSource == null)
            {
                MessageBox.Show("Please load some data first.");
                return;
            }

            // Finishes and save any pending changes to the given data
            dgvLearningSource.EndEdit();



            // Creates a matrix from the entire source data table
            double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);

            // Get only the input vector values (first two columns)
            double[][] inputs = table.GetColumns(0, 1).ToJagged();

            // Get only the output labels (last column)
            int[] outputs = table.GetColumn(2).ToInt32();


            // Creates a new instance of the SMO learning algorithm
            var smo = new SequentialMinimalOptimization<IKernel>()
            {
                // Set learning parameters
                Complexity = (double)numC.Value,
                Tolerance = (double)numT.Value,
                PositiveWeight = (double)numPositiveWeight.Value,
                NegativeWeight = (double)numNegativeWeight.Value,
                Kernel = createKernel()
            };


            try
            {
                // Run
                svm = smo.Learn(inputs, outputs);

                lbStatus.Text = "Training complete!";
            }
            catch (ConvergenceException)
            {
                lbStatus.Text = "Convergence could not be attained. "+
                    "The learned machine might still be usable.";
            }


            createSurface(table);


            // Check if we got support vectors
            if (svm.SupportVectors == null || svm.SupportVectors.Length == 0)
            {
                dgvSupportVectors.DataSource = null;
                graphSupportVectors.GraphPane.CurveList.Clear();
                return;
            }


            // Show support vectors on the Support Vectors tab page
            double[][] supportVectorsWeights = svm.SupportVectors.InsertColumn(svm.Weights);

            string[] supportVectorNames = columnNames.RemoveAt(columnNames.Length - 1).Concatenate("Weight");
            dgvSupportVectors.DataSource = new ArrayDataView(supportVectorsWeights, supportVectorNames);



            // Show the support vector labels on the scatter plot
            double[] supportVectorLabels = new double[svm.SupportVectors.Length];
            for (int i = 0; i < supportVectorLabels.Length; i++)
            {
                int j = inputs.Find(sv => sv == svm.SupportVectors[i])[0];
                supportVectorLabels[i] = outputs[j];
            }

            double[][] graph = svm.SupportVectors.InsertColumn(supportVectorLabels);

            CreateScatterplot(graphSupportVectors, graph.ToMatrix());


        }
コード例 #18
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        public void TransformTest()
        {
            var inputs = yinyang.Submatrix(null, 0, 1).ToArray();
            var labels = yinyang.GetColumn(2).ToInt32();
            
            ConfusionMatrix actual, expected;
            SequentialMinimalOptimization a, b;

            var kernel = new Polynomial(2, 0);

            {
                var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
                a = new SequentialMinimalOptimization(machine, inputs, labels);
                a.UseComplexityHeuristic = true;
                a.Run();

                int[] values = new int[labels.Length];
                for (int i = 0; i < values.Length; i++)
                    values[i] = Math.Sign(machine.Compute(inputs[i]));

                expected = new ConfusionMatrix(values, labels);
            }

            {
                var projection = inputs.Apply(kernel.Transform);
                var machine = new SupportVectorMachine(projection[0].Length);
                b = new SequentialMinimalOptimization(machine, projection, labels);
                b.UseComplexityHeuristic = true;
                b.Run();

                int[] values = new int[labels.Length];
                for (int i = 0; i < values.Length; i++)
                    values[i] = Math.Sign(machine.Compute(projection[i]));

                actual = new ConfusionMatrix(values, labels);
            }

            Assert.AreEqual(a.Complexity, b.Complexity, 1e-15);
            Assert.AreEqual(expected.TrueNegatives, actual.TrueNegatives);
            Assert.AreEqual(expected.TruePositives, actual.TruePositives);
            Assert.AreEqual(expected.FalseNegatives, actual.FalseNegatives);
            Assert.AreEqual(expected.FalsePositives, actual.FalsePositives);
        }
コード例 #19
0
ファイル: MainForm.cs プロジェクト: accord-net/framework
        /// <summary>
        ///   Creates a Support Vector Machine and teaches it to recognize
        ///   the previously loaded dataset using the current UI settings.
        /// </summary>
        /// 
        private void btnCreate_Click(object sender, EventArgs e)
        {
            if (dgvLearningSource.DataSource == null)
            {
                MessageBox.Show("Please load some data first.");
                return;
            }

            // Finishes and save any pending changes to the given data
            dgvLearningSource.EndEdit();



            // Creates a matrix from the entire source data table
            double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);

            // Get only the input vector values (first two columns)
            double[][] inputs = table.GetColumns(0, 1).ToArray();

            // Get only the output labels (last column)
            int[] outputs = table.GetColumn(2).ToInt32();

            // Create a sparse logistic learning algorithm
            var pcd = new ProbabilisticCoordinateDescent()
            {
                // Set learning parameters
                Complexity = (double)numC.Value,
                Tolerance = (double)numT.Value,
                PositiveWeight = (double)numPositiveWeight.Value,
                NegativeWeight = (double)numNegativeWeight.Value,
            };

            try
            {
                // Run
                svm = pcd.Learn(inputs, outputs);

                lbStatus.Text = "Training complete!";
            }
            catch (ConvergenceException)
            {
                lbStatus.Text = "Convergence could not be attained. " +
                    "The learned machine might still be usable.";
            }

            svm.Compress(); // reduce support vectors to a single weight vector
            Trace.Assert(svm.SupportVectors.Length == 1);
            Trace.Assert(svm.Weights.Length == 1);

            createSurface(table);

            // Show feature weight importance
            double[] weights = svm.SupportVectors[0].Abs();

            string[] featureNames = columnNames.RemoveAt(columnNames.Length - 1);
            dgvSupportVectors.DataSource = new ArrayDataView(weights, featureNames);

            CreateBarGraph(weights, featureNames);
        }
コード例 #20
0
ファイル: MainForm.cs プロジェクト: accord-net/framework
        private void btnCreate_Click(object sender, EventArgs e)
        {
            if (dgvLearningSource.DataSource == null)
            {
                MessageBox.Show("Please load some data first.");
                return;
            }

            // Finishes and save any pending changes to the given data
            dgvLearningSource.EndEdit();



            // Creates a matrix from the entire source data table
            double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);

            // Get only the input vector values (first two columns)
            double[][] inputs = table.GetColumns(0).ToArray();

            // Get only the outputs (last column)
            double[] outputs = table.GetColumn(1);


            // Create the specified Kernel
            IKernel kernel = createKernel();

            // Creates a new SMO for regression learning algorithm
            var smo = new SequentialMinimalOptimizationRegression()
            {
                // Set learning parameters
                Complexity = (double)numC.Value,
                Tolerance = (double)numT.Value,
                Epsilon = (double)numEpsilon.Value
            };


            try
            {
                // Run
                svm = smo.Learn(inputs, outputs);

                lbStatus.Text = "Training complete!";
            }
            catch (ConvergenceException)
            {
                lbStatus.Text = "Convergence could not be attained. " +
                    "The learned machine might still be usable.";
            }



            // Check if we got support vectors
            if (svm.SupportVectors.Length == 0)
            {
                dgvSupportVectors.DataSource = null;
                graphSupportVectors.GraphPane.CurveList.Clear();
                return;
            }



            // Show support vectors on the Support Vectors tab page
            double[][] supportVectorsWeights = svm.SupportVectors.InsertColumn(svm.Weights);

            string[] supportVectorNames = columnNames.RemoveAt(columnNames.Length - 1).Concatenate("Weight");
            dgvSupportVectors.DataSource = new ArrayDataView(supportVectorsWeights, supportVectorNames);



            // Show the support vector labels on the scatter plot
            double[] supportVectorLabels = new double[svm.SupportVectors.Length];
            for (int i = 0; i < supportVectorLabels.Length; i++)
            {
                int j = inputs.Find(sv => sv == svm.SupportVectors[i])[0];
                supportVectorLabels[i] = outputs[j];
            }

            double[][] graph = svm.SupportVectors.InsertColumn(supportVectorLabels);

            CreateScatterplot(graphSupportVectors, graph.ToMatrix());



            // Get the ranges for each variable (X and Y)
            DoubleRange range = table.GetColumn(0).GetRange();

            double[][] map = Vector.Interval(range, 0.05).ToArray();

            // Classify each point in the Cartesian coordinate system
            double[] result = map.Apply(svm.Compute);
            double[,] surface = map.ToMatrix().InsertColumn(result);

            CreateScatterplot(zedGraphControl2, surface);
        }
コード例 #21
0
        public void TrainTest2()
        {

            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] or =
            {
                -1,
                -1,
                -1,
                +1
            };

            // Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
            SupportVectorMachine machine = new SupportVectorMachine(inputs[0].Length);

            // Create the sequential minimal optimization teacher
            SequentialMinimalOptimization learn = new SequentialMinimalOptimization(machine, inputs, or);
            learn.Complexity = 1;

            // Run the learning algorithm
            learn.Run();


            double[] output = machine.Compute(inputs);

            for (int i = 0; i < output.Length; i++)
            {
                bool sor = or[i] >= 0;
                bool sou = output[i] >= 0;
                Assert.AreEqual(sor, sou);
            }


        }
コード例 #22
0
ファイル: Train.cs プロジェクト: KommuSoft/accord_framework
        public static void train_one(Problem prob, Parameters param, out double[] w, double Cp, double Cn)
        {
            double[][] inputs = prob.Inputs;
            int[] labels = prob.Outputs.Apply(x => x >= 0 ? 1 : -1);

            double eps = param.Tolerance;

            int pos = 0;
            for (int i = 0; i < labels.Length; i++)
                if (labels[i] >= 0) pos++;
            int neg = prob.Outputs.Length - pos;

            double primal_solver_tol = eps * Math.Max(Math.Min(pos, neg), 1.0) / prob.Inputs.Length;

            SupportVectorMachine svm = new SupportVectorMachine(prob.Dimensions);
            ISupportVectorMachineLearning teacher = null;


            switch (param.Solver)
            {
                case LibSvmSolverType.L2RegularizedLogisticRegression:

                    // l2r_lr_fun
                    teacher = new ProbabilisticNewtonMethod(svm, inputs, labels)
                    {
                        PositiveWeight = Cp,
                        NegativeWeight = Cn,
                        Tolerance = primal_solver_tol
                    }; break;


                case LibSvmSolverType.L2RegularizedL2LossSvc:

                    // fun_obj=new l2r_l2_svc_fun(prob, C);
                    teacher = new LinearNewtonMethod(svm, inputs, labels)
                    {
                        PositiveWeight = Cp,
                        NegativeWeight = Cn,
                        Tolerance = primal_solver_tol
                    }; break;


                case LibSvmSolverType.L2RegularizedL2LossSvcDual:

                    // solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L2LOSS_SVC_DUAL);
                    teacher = new LinearCoordinateDescent(svm, inputs, labels)
                    {
                        Loss = Loss.L2,
                        PositiveWeight = Cp,
                        NegativeWeight = Cn,
                    }; break;


                case LibSvmSolverType.L2RegularizedL1LossSvcDual:

                    // solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L1LOSS_SVC_DUAL);
                    teacher = new LinearCoordinateDescent(svm, inputs, labels)
                    {
                        Loss = Loss.L1,
                        PositiveWeight = Cp,
                        NegativeWeight = Cn,
                    }; break;


                case LibSvmSolverType.L1RegularizedLogisticRegression:

                    // solve_l1r_lr(&prob_col, w, primal_solver_tol, Cp, Cn);
                    teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels)
                    {
                        PositiveWeight = Cp,
                        NegativeWeight = Cn,
                        Tolerance = primal_solver_tol
                    }; break;


                case LibSvmSolverType.L2RegularizedLogisticRegressionDual:

                    // solve_l2r_lr_dual(prob, w, eps, Cp, Cn);
                    teacher = new ProbabilisticDualCoordinateDescent(svm, inputs, labels)
                    {
                        PositiveWeight = Cp,
                        NegativeWeight = Cn,
                        Tolerance = primal_solver_tol,
                    }; break;
            }


            Trace.WriteLine("Training " + param.Solver);
            
            // run the learning algorithm
            var sw = Stopwatch.StartNew();
            double error = teacher.Run();
            sw.Stop();

            // save the solution
            w = svm.ToWeights();

            Trace.WriteLine(String.Format("Finished {0}: {1} in {2}", 
                param.Solver, error, sw.Elapsed));
        }
コード例 #23
0
        public void SequentialMinimalOptimizationConstructorTest()
        {
            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] or =
            {
                0,
                0,
                0,
                +1
            };

            // Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
            SupportVectorMachine machine = new SupportVectorMachine(inputs[0].Length);

            var learn = new SequentialMinimalOptimization(machine, inputs, or);
            learn.Run();

            for (int i = 0; i < inputs.Length; i++)
            {
                bool actual = machine.Decide(inputs[i]);
                Assert.AreEqual(or[i] > 0, actual);
            }
        }
コード例 #24
0
        /// <summary>
        ///   Creates a new <see cref="SupportVectorMachine"/> that is
        ///   completely equivalent to a <see cref="LogisticRegression"/>.
        /// </summary>
        /// 
        /// <param name="regression">The <see cref="LogisticRegression"/> to be converted.</param>
        /// 
        /// <returns>
        ///   A <see cref="SupportVectorMachine"/> whose linear weights are
        ///   equivalent to the given <see cref="LogisticRegression"/>'s
        ///   <see cref="GeneralizedLinearRegression.Coefficients"> linear 
        ///   coefficients</see>, properly configured with a <see cref="LogLinkFunction"/>. 
        /// </returns>
        /// 
        public static SupportVectorMachine FromLogisticRegression(LogisticRegression regression)
        {
            double[] weights = regression.Coefficients;
            var svm = new SupportVectorMachine(regression.Inputs);
            for (int i = 0; i < svm.weights.Length; i++)
                svm.Weights[i] = weights[i + 1];

            svm.Threshold = regression.Intercept;
            svm.Link = new LogitLinkFunction(1, 0);

            return svm;
        }
コード例 #25
0
        public void KernelTest1()
        {
            var dataset = SequentialMinimalOptimizationTest.yinyang;
            double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
            int[] labels = dataset.GetColumn(2).ToInt32();

            double e1, e2;
            double[] w1, w2;

            {
                Accord.Math.Tools.SetupGenerator(0);
                var svm = new SupportVectorMachine(inputs: 2);
                var teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels);

                teacher.Tolerance = 1e-10;
                teacher.Complexity = 1e+10;

                e1 = teacher.Run();
                w1 = svm.ToWeights();
            }

            {
                Accord.Math.Tools.SetupGenerator(0);
                var svm = new KernelSupportVectorMachine(new Linear(0), inputs: 2);
                var teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels);

                teacher.Tolerance = 1e-10;
                teacher.Complexity = 1e+10;

                e2 = teacher.Run();
                w2 = svm.ToWeights();
            }

            Assert.AreEqual(e1, e2);
            Assert.AreEqual(w1.Length, w2.Length);
            Assert.AreEqual(w1[0], w2[0]);
            Assert.AreEqual(w1[1], w2[1]);
            Assert.AreEqual(w1[2], w2[2]);
        }
コード例 #26
0
        /// <summary>
        ///   Creates a new linear <see cref="SupportVectorMachine"/> 
        ///   with the given set of linear <paramref name="weights"/>.
        /// </summary>
        /// 
        /// <param name="weights">The machine's linear coefficients.</param>
        /// 
        /// <returns>
        ///   A <see cref="SupportVectorMachine"/> whose linear coefficients
        ///   are defined by the given <paramref name="weights"/> vector.
        /// </returns>
        /// 
        public static SupportVectorMachine FromWeights(double[] weights)
        {
            var svm = new SupportVectorMachine(weights.Length - 1);

            svm.Weights = new double[svm.Inputs];
            for (int i = 0; i < svm.weights.Length; i++)
                svm.Weights[i] = weights[i + 1];
            svm.Threshold = weights[0];

            return svm;
        }
コード例 #27
0
        public void LearnTest5()
        {

            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] positives =
            {
                1,
                1,
                1,
                1
            };

            // Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
            SupportVectorMachine machine = new SupportVectorMachine(inputs[0].Length);

            // Create the sequential minimal optimization teacher
            SequentialMinimalOptimization learn = new SequentialMinimalOptimization(machine, inputs, positives);
            learn.Complexity = 1;

            // Run the learning algorithm
            double error = learn.Run();

            Assert.AreEqual(0, error);


            int[] output = inputs.Apply(p => (int)machine.Compute(p));

            for (int i = 0; i < output.Length; i++)
            {
                bool sor = positives[i] >= 0;
                bool sou = output[i] >= 0;
                Assert.AreEqual(sor, sou);
            }
        }
コード例 #28
0
        public void GridsearchConstructorTest2()
        {
            Accord.Math.Tools.SetupGenerator(0);

            // Example binary data
            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] xor = // xor labels
            {
                -1, 1, 1, -1
            };

            // Declare the parameters and ranges to be searched
            GridSearchRange[] ranges = 
            {
                new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 1000000, 0.50 } ),
            };


            // Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
            var gridsearch = new GridSearch<SupportVectorMachine>(ranges);

            // Set the fitting function for the algorithm
            gridsearch.Fitting = delegate(GridSearchParameterCollection parameters, out double error)
            {
                // The parameters to be tried will be passed as a function parameter.
                double complexity = parameters["complexity"].Value;

                // Use the parameters to build the SVM model
                SupportVectorMachine svm = new SupportVectorMachine(2);

                // Create a new learning algorithm for SVMs
                SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, xor);
                smo.Complexity = complexity;

                // Measure the model performance to return as an out parameter
                error = smo.Run();

                return svm; // Return the current model
            };


            {
                // Declare some out variables to pass to the grid search algorithm
                GridSearchParameterCollection bestParameters; double minError;

                // Compute the grid search to find the best Support Vector Machine
                SupportVectorMachine bestModel = gridsearch.Compute(out bestParameters, out minError);


                // The minimum error should be zero because the problem is well-known.
                Assert.AreEqual(minError, 0.5);


                Assert.IsNotNull(bestModel);
                Assert.IsNotNull(bestParameters);
                Assert.AreEqual(bestParameters.Count, 1);
            }

            {
                // Compute the grid search to find the best Support Vector Machine
                var result = gridsearch.Compute();


                // The minimum error should be zero because the problem is well-known.
                Assert.AreEqual(result.Error, 0.5);

                Assert.IsNotNull(result.Model);
                Assert.AreEqual(5, result.Errors.Length);
                Assert.AreEqual(5, result.Models.Length);
            }
        }
コード例 #29
0
        public void ComputeTest5()
        {
            var dataset = SequentialMinimalOptimizationTest.yinyang;

            double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
            int[] labels = dataset.GetColumn(2).ToInt32();

            var kernel = new Polynomial(2, 1);

            Accord.Math.Tools.SetupGenerator(0);
            var projection = inputs.Apply(kernel.Transform);
            var machine = new SupportVectorMachine(projection[0].Length);
            var smo = new LinearCoordinateDescent(machine, projection, labels)
            {
                Complexity = 1000000,
                Tolerance = 1e-15
            };

            double error = smo.Run();

            Assert.AreEqual(1000000.0, smo.Complexity, 1e-15);

            int[] actual = new int[labels.Length];
            for (int i = 0; i < actual.Length; i++)
                actual[i] = Math.Sign(machine.Compute(projection[i]));

            ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
            Assert.AreEqual(6, matrix.FalseNegatives);
            Assert.AreEqual(7, matrix.FalsePositives);
            Assert.AreEqual(44, matrix.TruePositives);
            Assert.AreEqual(43, matrix.TrueNegatives);
        }
コード例 #30
0
        public void LeastSquaresConstructorTest()
        {
            double[][] inputs =
            {
                new double[] { -1, -1 },
                new double[] { -1,  1 },
                new double[] {  1, -1 },
                new double[] {  1,  1 }
            };

            int[] or =
            {
                0,
                0,
                0,
                +1
            };

            // Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
            var machine = new SupportVectorMachine(inputs[0].Length);

            var learn = new LeastSquaresLearning(machine, inputs, or);

            double error = learn.Run();
            Assert.AreEqual(0, error);

            {
                int[] iout = new int[inputs.Length];
                machine.ToMulticlass().Decide(inputs, iout);
                for (int i = 0; i < iout.Length; i++)
                    Assert.AreEqual(or[i], iout[i]);
            }
            {
                double[] dout = new double[inputs.Length];
                machine.ToMulticlass().Decide(inputs, dout);
                for (int i = 0; i < dout.Length; i++)
                    Assert.AreEqual(or[i], dout[i]);
            }
            {
                bool[] bout = new bool[inputs.Length];
                machine.Decide(inputs, bout);
                Assert.IsFalse(bout[0]);
                Assert.IsFalse(bout[1]);
                Assert.IsFalse(bout[2]);
                Assert.IsTrue(bout[3]);
            }
            {
                int[][] iiout = Jagged.Create<int>(inputs.Length, 2);
                machine.ToMulticlass().Decide(inputs, iiout);
                for (int i = 0; i < iiout.Length; i++)
                {
                    Assert.AreEqual(or[i], iiout[i][0]);
                    Assert.AreEqual(or[i], iiout[i][1] == 1 ? 0 : 1);
                }
            }
            {
                bool[][] bbout = Jagged.Create<bool>(inputs.Length, 2);
                machine.ToMulticlass().Decide(inputs, bbout);
                for (int i = 0; i < bbout.Length; i++)
                {
                    Assert.AreEqual(or[i], bbout[i][0] ? 1 : 0);
                    Assert.AreEqual(or[i], bbout[i][1] ? 0 : 1);
                }
            }
        }