Exemple #1
0
        /// <summary>
        /// To scale the test data
        /// </summary>
        /// <param name="orig_data">Original test data, use NaN for missing features.</param>
        /// <param name="targe_min">The target min</param>
        /// <param name="targe_max">The target max</param>
        /// <param name="features_min">The minimums of all features</param>
        /// <param name="features_max">The maximums of all features</param>
        /// <returns>The scaled test data in LibSVM format.</returns>
        public static LibSVMNode[] ScaleData(double[] orig_data, double targe_min, double targe_max, double[] features_min, double[] features_max)
        {
            int feature_n = features_min.Length;

            double[] k = new double[feature_n];
            for (int i = 0; i < feature_n; i++)
            {
                k[i] = (targe_max - targe_min) / (features_max[i] - features_min[i]);
            }

            List <LibSVMNode> scaled_data = new List <LibSVMNode>(); LibSVMNode node;

            for (int i = 0; i < feature_n; i++)
            {
                if (double.IsNaN(orig_data[i]))
                {
                    continue;
                }

                node       = new LibSVMNode();
                node.index = i + 1;
                node.value = k[i] * (orig_data[i] - features_min[i]) + targe_min;
                scaled_data.Add(node);
            }
            node = new LibSVMNode(); node.index = -1; scaled_data.Add(node);
            return(scaled_data.ToArray());
        }
Exemple #2
0
        /// <summary>
        /// To scale the test data
        /// </summary>
        /// <param name="orig_data">Original test data, use NaN for missing features.</param>
        /// <param name="targe_min">The target min</param>
        /// <param name="targe_max">The target max</param>
        /// <param name="features_min">The minimums of all features</param>
        /// <param name="features_max">The maximums of all features</param>
        /// <returns>The scaled test data in LibSVM format.</returns>
        public static LibSVMNode[][] ScaleData(List <List <double> > orig_data, double targe_min, double targe_max, double[] features_min, double[] features_max)
        {
            int feature_n = features_min.Length;

            double[] k = new double[feature_n];
            for (int i = 0; i < feature_n; i++)
            {
                k[i] = (targe_max - targe_min) / (features_max[i] - features_min[i]);
            }

            int sample_n = orig_data.Count;

            LibSVMNode[][] data = new LibSVMNode[sample_n][];
            for (int m = 0; m < sample_n; m++)
            {
                List <LibSVMNode> scaled_data = new List <LibSVMNode>(); LibSVMNode node;
                for (int i = 0; i < feature_n; i++)
                {
                    if (double.IsNaN(orig_data[m][i]))
                    {
                        continue;
                    }

                    node       = new LibSVMNode();
                    node.index = i + 1;
                    node.value = k[i] * (orig_data[m][i] - features_min[i]) + targe_min;
                    scaled_data.Add(node);
                }
                node    = new LibSVMNode(); node.index = -1; scaled_data.Add(node);
                data[m] = scaled_data.ToArray();
            }
            return(data);
        }
Exemple #3
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 /// <summary>
 /// Convert sample features to LibSVM format
 /// </summary>
 /// <param name="sample_s">sample feature in string, i.e., one record in LibSVM file. "Target index:value index:value ..."</param>
 /// <param name="sample">the converted sample feature in LibSVM format.</param>
 /// <param name="label">the label</param>
 public static void ToLibSVMFormat(string sample_s, out LibSVMNode[] sample, out double label)
 {
     string[] words = sample_s.Trim().Split();
     label  = double.Parse(words[0]);
     sample = new LibSVMNode[words.Length];
     for (int i = 1; i < words.Length; i++)
     {
         string[] ws = words[i].Split(new char[] { ':' });
         sample[i - 1].index = int.Parse(ws[0]);
         sample[i - 1].value = double.Parse(ws[1]);
     }
     sample[words.Length - 1].index = -1;
 }
Exemple #4
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        /// <summary>
        /// Convert the SVM Linear model to single vector representation
        /// </summary>
        public unsafe float[] ToSingleVector()
        {
            LibSVMNode[][] support_vectors = new LibSVMNode[nrSV][];
            for (int i = 0; i < nrSV; i++)
            {
                LibSVMNode[] sv = new LibSVMNode[nrFeature];
                fixed(LibSVMNode *sv_p = sv)
                {
                    svm_get_sv(svm_model, i, new IntPtr(sv_p));
                }

                support_vectors[i] = sv;
            }

            double rho = svm_get_rho(svm_model);

            double[] coef = new double[nrSV];
            fixed(double *coef_p = coef)
            {
                svm_get_coef(svm_model, new IntPtr(coef_p));
            }

            float[] single_vector = new float[nrFeature + 1]; int index; double value;
            for (int i = 0; i < nrSV; i++)
            {
                for (int j = 0; j < support_vectors[i].Length; j++)
                {
                    index = support_vectors[i][j].index;
                    if (index == -1)
                    {
                        break;
                    }
                    value = support_vectors[i][j].value;

                    single_vector[index - 1] += (float)(value * coef[i]);
                }
            }
            single_vector[nrFeature] = -(float)rho;

            return(single_vector);
        }
Exemple #5
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        /// <summary>
        /// To scale the test data
        /// </summary>
        /// <param name="orig_data">Original test data in LibSVM format.</param>
        /// <param name="targe_min">The target min</param>
        /// <param name="targe_max">The target max</param>
        /// <param name="features_min">The minimums of all features</param>
        /// <param name="features_max">The maximums of all features</param>
        /// <returns>The scaled test data in LibSVM format.</returns>
        public static LibSVMNode[] ScaleData(LibSVMNode[] orig_data, double targe_min, double targe_max, double[] features_min, double[] features_max)
        {
            int feature_n = features_min.Length;

            double[] k = new double[feature_n];
            for (int i = 0; i < feature_n; i++)
            {
                k[i] = (targe_max - targe_min) / (features_max[i] - features_min[i]);
            }

            int n = orig_data.Length;

            LibSVMNode[] scaled_data = new LibSVMNode[n];
            for (int i = 0; i < n; i++)
            {
                int index = orig_data[i].index;
                scaled_data[i].index = index; if (index == -1)
                {
                    break;
                }
                scaled_data[i].value = k[index - 1] * (orig_data[i].value - features_min[index - 1]) + targe_min;
            }
            return(scaled_data);
        }