Exemple #1
0
        public double Overlap(int[] sdr1, int[] sdr2)
        {
            Assert.AreEqual(sdr1.Length, sdr2.Length);
            int sum = ArrayUtils.Sum(ArrayUtils.And(sdr1, sdr2));

            return(sum / (double)ArrayUtils.Sum(sdr1));
        }
Exemple #2
0
        public double Overlap(int[] sdr1, int[] sdr2)
        {
            Assert.AreEqual(sdr1.Length, sdr2.Length);
            int sum = ArrayUtils.Sum(ArrayUtils.And(sdr1, sdr2));

            //		Console.WriteLine("and = " + Arrays.toString(ArrayUtils.where(ArrayUtils.and(sdr1, sdr2), ArrayUtils.WHERE_1)));
            //		Console.WriteLine("sum = " + ArrayUtils.sum(ArrayUtils.and(sdr1, sdr2)));
            return((double)sum / (double)ArrayUtils.Sum(sdr1));
        }
        private static void NonzeroFilterGroup(int minValids, bool percentage, IMatrixData mdata, Parameters param,
                                               bool oneGroup, double threshold, double threshold2, FilteringMode filterMode, IList <string[]> groupCol
                                               )
        {
            List <int> valids    = new List <int>();
            List <int> notvalids = new List <int>();

            string[] groupVals = ArrayUtils.UniqueValuesPreserveOrder(groupCol);
            Array.Sort(groupVals);
            int[][] groupInds = CalcGroupInds(groupVals, groupCol);
            for (int i = 0; i < mdata.RowCount; i++)
            {
                int[] counts = new int[groupVals.Length];
                int[] totals = new int[groupVals.Length];
                for (int j = 0; j < groupInds.Length; j++)
                {
                    for (int k = 0; k < groupInds[j].Length; k++)
                    {
                        if (groupInds[j][k] >= 0)
                        {
                            totals[groupInds[j][k]]++;
                        }
                    }
                    if (PerseusPluginUtils.IsValid(mdata.Values.Get(i, j), threshold, threshold2, filterMode))
                    {
                        for (int k = 0; k < groupInds[j].Length; k++)
                        {
                            if (groupInds[j][k] >= 0)
                            {
                                counts[groupInds[j][k]]++;
                            }
                        }
                    }
                }
                bool[] groupValid = new bool[counts.Length];
                for (int j = 0; j < groupValid.Length; j++)
                {
                    groupValid[j] = PerseusPluginUtils.Valid(counts[j], minValids, percentage, totals[j]);
                }
                if (oneGroup ? ArrayUtils.Or(groupValid) : ArrayUtils.And(groupValid))
                {
                    valids.Add(i);
                }
                else
                {
                    notvalids.Add(i);
                }
            }
            PerseusPluginUtils.FilterRowsNew(mdata, param, valids.ToArray());
        }
        public static bool IsValidRowNumFilter(double[] row, Relation[] relations, bool and)
        {
            Dictionary <int, double> vars = new Dictionary <int, double>();

            for (int j = 0; j < row.Length; j++)
            {
                vars.Add(j, row[j]);
            }
            bool[] results = new bool[relations.Length];
            for (int j = 0; j < relations.Length; j++)
            {
                results[j] = relations[j].NumEvaluateDouble(vars);
            }
            return(and ? ArrayUtils.And(results) : ArrayUtils.Or(results));
        }
Exemple #5
0
        /**
         * Does a bitwise compare of the two bitmaps and returns a fractional
         * value between 0 and 1 of how similar they are.
         * 1 => identical
         * 0 => no overlapping bits
         * IGNORES difference in length (only compares bits of shorter list)  e..g 11 and 1100101010 are "identical"
         * @see org.numenta.nupic.encoders.Encoder#closenessScores(gnu.trove.list.TDoubleList, gnu.trove.list.TDoubleList, boolean)
         */
        public override List <double> ClosenessScores(List <double> expValues, List <double> actValues, bool fractional)
        {
            List <double> result = new List <double>();

            double ratio       = 1.0d;
            double expectedSum = expValues.Sum();
            double actualSum   = actValues.Sum();

            if (actualSum > expectedSum)
            {
                double diff = actualSum - expectedSum;
                if (diff < expectedSum)
                {
                    ratio = 1 - diff / expectedSum;
                }
                else
                {
                    ratio = 1 / diff;
                }
            }

            int[] expectedInts = ArrayUtils.ToIntArray(expValues.ToArray());
            int[] actualInts   = ArrayUtils.ToIntArray(actValues.ToArray());

            int[] overlap = ArrayUtils.And(expectedInts, actualInts);

            int    overlapSum = ArrayUtils.Sum(overlap);
            double r          = 0.0;

            if (expectedSum != 0)
            {
                r = overlapSum / expectedSum;
            }
            r = r * ratio;

            if (LOGGER.IsDebugEnabled)
            {
                LOGGER.Debug(string.Format("closenessScores for expValues: {0} and actValues: {1} is: {2}", Arrays.ToString(expectedInts), actualInts, r));
            }

            result.Add(r);
            return(result);
        }
Exemple #6
0
        public void TestAnd()
        {
            int[] a      = new int[] { 0, 0, 0, 0, 1, 1, 1 };
            int[] b      = new int[] { 0, 0, 0, 0, 1, 1, 1 };
            int[] result = ArrayUtils.And(a, b);
            Assert.IsTrue(Arrays.AreEqual(a, result));

            a      = new int[] { 0, 0, 0, 0, 1, 0, 1 };
            result = ArrayUtils.And(a, b);
            Assert.IsTrue(Arrays.AreEqual(a, result));

            a      = new int[] { 0, 0, 0, 0, 0, 0, 0 };
            result = ArrayUtils.And(a, b);
            Assert.IsTrue(Arrays.AreEqual(a, result));

            a = new int[] { 1, 1, 1, 1, 0, 0, 0 };
            int[] expected = new int[] { 0, 0, 0, 0, 0, 0, 0 };
            result = ArrayUtils.And(a, b);
            Assert.IsTrue(Arrays.AreEqual(expected, result));
        }
        public void ProcessData(IMatrixData mdata, Parameters param, ref IMatrixData[] supplTables,
                                ref IDocumentData[] documents, ProcessInfo processInfo)
        {
            const bool rows      = true;
            int        minValids = PerseusPluginUtils.GetMinValids(param, out bool percentage);
            ParameterWithSubParams <int> modeParam = param.GetParamWithSubParams <int>("Mode");
            int modeInd = modeParam.Value;

            if (modeInd != 0 && mdata.CategoryRowNames.Count == 0)
            {
                processInfo.ErrString = "No grouping is defined.";
                return;
            }
            PerseusPluginUtils.ReadValuesShouldBeParams(param, out FilteringMode filterMode, out double threshold, out double threshold2);
            if (modeInd != 0)
            {
                int        gind     = modeParam.GetSubParameters().GetParam <int>("Grouping").Value;
                string[][] groupCol = mdata.GetCategoryRowAt(gind);
                if (param.GetParam <int>("Filter mode").Value == 2)
                {
                    //discarded
                    List <int> valids    = new List <int>();
                    List <int> notvalids = new List <int>();
                    string[]   groupVals = ArrayUtils.UniqueValuesPreserveOrder(groupCol);
                    Array.Sort(groupVals);
                    int[][] groupInds = CalcGroupInds(groupVals, groupCol);
                    for (int i = 0; i < mdata.RowCount; i++)
                    {
                        int[] counts = new int[groupVals.Length];
                        int[] totals = new int[groupVals.Length];
                        for (int j = 0; j < groupInds.Length; j++)
                        {
                            for (int k = 0; k < groupInds[j].Length; k++)
                            {
                                if (groupInds[j][k] >= 0)
                                {
                                    totals[groupInds[j][k]]++;
                                }
                            }
                            if (PerseusPluginUtils.IsValid(mdata.Values.Get(i, j), threshold, threshold2, filterMode))
                            {
                                for (int k = 0; k < groupInds[j].Length; k++)
                                {
                                    if (groupInds[j][k] >= 0)
                                    {
                                        counts[groupInds[j][k]]++;
                                    }
                                }
                            }
                        }
                        bool[] groupValid = new bool[counts.Length];
                        for (int j = 0; j < groupValid.Length; j++)
                        {
                            groupValid[j] = PerseusPluginUtils.Valid(counts[j], minValids, percentage, totals[j]);
                        }
                        if (modeInd == 2 ? ArrayUtils.Or(groupValid) : ArrayUtils.And(groupValid))
                        {
                            valids.Add(i);
                        }
                        else
                        {
                            notvalids.Add(i);
                        }
                    }
                    supplTables = new[] { PerseusPluginUtils.CreateSupplTabSplit(mdata, notvalids.ToArray()) };
                }
                NonzeroFilterGroup(minValids, percentage, mdata, param, modeInd == 2, threshold, threshold2, filterMode, groupCol);
            }
            else
            {
                if (param.GetParam <int>("Filter mode").Value == 2)
                {
                    supplTables = new[] { PerseusPluginUtils.NonzeroFilter1Split(rows, minValids, percentage, mdata, param, threshold, threshold2, filterMode) };
                }
                PerseusPluginUtils.NonzeroFilter1(rows, minValids, percentage, mdata, param, threshold, threshold2, filterMode);
            }
        }