Esempio n. 1
0
        private static string[] ColumnNames(bool withLabel, ModelValue prediction)
        {
            var columns = new List <string>();
            var data    = new ModelDataSet();

            for (int i = 0; i < data.Features(); i++)
            {
                columns.Add(data.Name(i));
            }

            if (withLabel)
            {
                switch (prediction)
                {
                case ModelValue.Action: columns.Add("Action"); break;

                case ModelValue.Angle: columns.Add("FaceAngle"); break;

                case ModelValue.XY: columns.Add("MoveAngle"); break;

                default: throw new Exception("Unknown value for prediction : " + prediction);
                }
            }

            return(columns.ToArray());
        }
        public void SetLevel1UnitValueTest()
        {
            //test Level 2
            Model      model = new Model();
            ModelValue mv    = new ModelValue();

            SetModelValueSample(mv);
            //calculate Level 3 values
            mv.CalculateLevel4Values();
            mv.CalculateLevel3Values();
            //create model
            model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = true;
            //model.TrimModel();
            model.CalculateModelWeight();
            mv.SetLevel4UnitValue(model.level4_nodes);
            mv.SetLevel3UnitValue(model.level3_nodes);
            mv.SetLevel2UnitValue(model.level2_nodes);
            mv.SetLevel1UnitValue(model.level1_nodes);
            //verify the value
            Assert.AreEqual(3.1944350372696791127913786374672, (double)model.level1_nodes[0].value, 0.0001);

            ////修剪模型的情况
            //model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = false;
            //model.TrimModel();
            //model.CalculateModelWeight();
            //mv.SetLevel4UnitValue(model.level4_nodes);
            //mv.SetLevel3UnitValue(model.level3_nodes);
            //mv.SetLevel2UnitValue(model.level2_nodes);
            //mv.SetLevel1UnitValue(model.level1_nodes);
            ////验证数值
            //Assert.AreEqual(2.8009873001760972650572709221841, (double)model.level1_nodes[0].value, 0.0001);
        }
        public void CalculateLevel3ValuesTest()
        {
            //build Model's Dictionary
            ModelValue mv = new ModelValue();

            SetModelValueSample(mv);
            //calculate Level 3 values
            mv.CalculateLevel4Values();
            mv.CalculateLevel3Values();
            //verify calculation results
            Assert.AreEqual(mv.NDCS_TOTAL / mv.NS, mv.Coupling);
            Assert.AreEqual(mv.NS / mv.NM, mv.Cohesion);
            Assert.AreEqual(mv.NIS_TOTAL / (mv.NS * mv.NS - mv.NS), mv.Design_Complexity);
            Assert.AreEqual((mv.NPI_TOTAL + mv.NII_TOTAL) / mv.NS, mv.System_Size);
            Assert.AreEqual((mv.NPI_TOTAL * mv.NPI_TOTAL) / (mv.NM * mv.NM), mv.Service_Granularity);
            Assert.AreEqual((mv.NMP_TOTAL * mv.NMP_TOTAL) / (mv.NPI_TOTAL * mv.NPI_TOTAL), mv.Parameter_Granularity);
            Assert.AreEqual(mv.WISL_TOTAL / mv.NS, mv.Service_Loopback);

            Assert.AreEqual(17.28380m / 12.23456m, mv.Coupling);
            Assert.AreEqual(12.23456m / 23.45678m, mv.Cohesion);
            Assert.AreEqual(42.71615m / (12.23456m * 12.23456m - 12.23456m), mv.Design_Complexity);
            Assert.AreEqual((67.28380m + 92.71615m) / 12.23456m, mv.System_Size);
            Assert.AreEqual((67.28380m * 67.28380m) / (23.45678m * 23.45678m), mv.Service_Granularity);
            Assert.AreEqual((117.28380m * 117.28380m) / (67.28380m * 67.28380m), mv.Parameter_Granularity);
            Assert.AreEqual(3 / 12.23456m, mv.Service_Loopback);
        }
Esempio n. 4
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        private static string WriteToDisk(IEnumerable <ModelDataSet> data, ModelValue prediction)
        {
            // geneate a random path
            var path = Path.Combine(Path.GetTempPath(), Path.GetTempFileName());

            using (var writer = File.CreateText(path))
            {
                foreach (var d in data)
                {
                    for (int i = 0; i < d.Features(); i++)
                    {
                        writer.Write(d.Feature(i));
                        writer.Write(',');
                    }
                    switch (prediction)
                    {
                    case ModelValue.Action: writer.WriteLine(d.Action); break;

                    case ModelValue.Angle: writer.WriteLine(d.FaceAngle); break;

                    case ModelValue.XY: writer.WriteLine(d.MoveAngle); break;

                    default: throw new Exception("Unknown value for prediction : " + prediction);
                    }
                }
            }

            return(path);
        }
Esempio n. 5
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        private static TextLoader GetTextLoader(MLContext context, ModelValue prediction)
        {
            var index = 0;

            return(context.Data.CreateTextLoader(
                       columns: ColumnNames(true /*with label*/, prediction).Select(c => new TextLoader.Column(c, DataKind.Single, index++)).ToArray(),
                       separatorChar: ',',
                       hasHeader: false));
        }
Esempio n. 6
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        private void BindToModelValue(EAV.Model.IModelAttribute attribute, EAV.Model.IModelInstance instance, EAV.Model.IModelValue value, ViewModelAttributeValue viewValue)
        {
            if (value == null && viewValue == null)
            {
                return;
            }

            if (value != null && viewValue == null)
            {
                if (value.ObjectState == ObjectState.New)
                {
                    instance.Values.Remove(value);
                }
                else
                {
                    value.MarkDeleted();
                }

                return;
            }
            else if (value == null && viewValue != null)
            {
                value = new ModelValue()
                {
                    Attribute = attribute, Instance = instance, Unit = attribute.VariableUnits.HasValue ? new ModelUnit()
                    {
                        UnitID = NextUnitID
                    } : null
                };
            }

            if (value.RawValue != viewValue.Value)
            {
                value.RawValue = viewValue.Value;
            }

            if (attribute.VariableUnits.HasValue)
            {
                if (attribute.VariableUnits.Value)
                {
                    if (value.Unit.ObjectState == ObjectState.New && !String.Equals(value.Unit.DisplayText, viewValue.UnitText, StringComparison.InvariantCulture))
                    {
                        value.Unit.DisplayText = viewValue.UnitText;
                    }
                }
                else
                {
                    if (value.UnitID != viewValue.UnitID)
                    {
                        value.Unit = attribute.Units.SingleOrDefault(it => it.UnitID == viewValue.UnitID);
                    }
                }
            }
        }
Esempio n. 7
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    public void Execute(int s, ref int sP, ref int steps)
    {
        int lclsteps = 0;

        sP = s;
        double r_temp = 0;
        //steps--;
        int max = 5;

        while (sP != SubGoalState)
        {
            int[] temp = maxA(sP);
            int   a    = temp[1];
            s = sP;
            if (temp[0] == -1)
            {
                break;// theres no choice in initiation set from this state
            }
            //sP = temp[0];
            bool contextChanged = m_agent.executeAction(sP, temp[1], ref sP, ref r_temp, ref steps);
            if (sP == s)
            {
                max--;
                if (max == 0)
                {
                    break;
                }
            }
            else
            {
                max = 5;
            }
            if (contextChanged || temp[0] != sP)
            {
                break;
            }
            if (m_agent.Model[sP, temp[1]] == null)
            {
                ModelValue m = new ModelValue(s, a, sP, r_temp, 1);
                m_agent.Model[s, a] = m;
            }
            else
            {
                m_agent.Model[s, a].Update(s, a, sP, r_temp, 1);
            }
            //steps++;
            lclsteps++;
            if (lclsteps > 100)
            {
                break;
            }
        }
        //steps++;
    }
Esempio n. 8
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        private static TextLoader GetTextLoader(MLContext context, ModelValue prediction)
        {
            var index = 0;

            return(context.Data.TextReader(
                       new TextLoader.Arguments()
            {
                Separator = ",",
                HasHeader = false,
                Column = ColumnNames(true /*with label*/, prediction).Select(c => new TextLoader.Column(c, DataKind.R4, index++)).ToArray()
            }));
        }
Esempio n. 9
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        // train
        public ModelMLNet(IEnumerable <ModelDataSet> data, ModelValue prediction)
        {
            Context = new MLContext();

            // add data
            var textLoader = GetTextLoader(Context, prediction);

            // spill to disk !?!?! since there is no way to load from a collection
            var pathToData = "";

            try
            {
                // write data to disk
                pathToData = WriteToDisk(data, prediction);

                // read in data
                IDataView dataView = textLoader.Load(pathToData);
                InputSchema = dataView.Schema;

                // configurations
                var label = "";
                switch (prediction)
                {
                case ModelValue.Action: label = "Action"; break;

                case ModelValue.Angle: label = "FaceAngle"; break;

                case ModelValue.XY: label = "MoveAngle"; break;

                default: throw new Exception("Unknown value for prediction : " + prediction);
                }
                var dataPipeline = Context.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: label)
                                   .Append(Context.Transforms.Concatenate("Features", ColumnNames(false /* do not include the label */, prediction)));

                // set the training algorithm
                var trainer          = Context.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
                var trainingPipeline = dataPipeline.Append(trainer);

                TrainedModel = trainingPipeline.Fit(dataView);
            }
            finally
            {
                // cleanup
                if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
                {
                    File.Delete(pathToData);
                }
            }
        }
Esempio n. 10
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        public override ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
        {
            if (TrainedModel == null)
            {
                throw new Exception("Must initialize the model before calling");
            }

            lock (TrainedModel)
            {
#if ML_LEGACY
                var testData  = CollectionDataSource.Create(data);
                var evaluator = new RegressionEvaluator();
                var metrics   = evaluator.Evaluate(TrainedModel, testData);

                return(new ModelFitness()
                {
                    RMS = metrics.Rms,
                    RSquared = metrics.RSquared
                });
#else
                var textLoader = GetTextLoader(Context, prediction);

                var pathToData = "";
                try
                {
                    // ugh have to spill data to disk for it to work!
                    pathToData = WriteToDisk(data, prediction);

                    IDataView dataView    = textLoader.Read(pathToData);
                    var       predictions = TrainedModel.Transform(dataView);
                    var       metrics     = Context.Regression.Evaluate(predictions, label: "Label", score: "Score");

                    return(new ModelFitness()
                    {
                        RMS = metrics.Rms,
                        RSquared = metrics.RSquared
                    });
                }
                finally
                {
                    // cleanup
                    if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
                    {
                        File.Delete(pathToData);
                    }
                }
#endif
            }
        }
        public void CalculateLevel4ValuesTest()
        {
            //build Model's Dictionary
            ModelValue mv = new ModelValue();

            SetModelValueSample(mv);
            //calculate Level 4 values
            mv.CalculateLevel4Values();
            //verify calculation results
            Assert.AreEqual(12.23456m, mv.NS);
            Assert.AreEqual(17.28380m, mv.NDCS_TOTAL);
            Assert.AreEqual(23.45678m, mv.NM);
            Assert.AreEqual(42.71615m, mv.NIS_TOTAL);
            Assert.AreEqual(67.28380m, mv.NPI_TOTAL);
            Assert.AreEqual(92.71615m, mv.NII_TOTAL);
            Assert.AreEqual(117.28380m, mv.NMP_TOTAL);
            Assert.AreEqual(3, mv.WISL_TOTAL);
        }
 public void SetModelValueSample(ModelValue mv)
 {
     //add service name
     mv.Dic_NO_ServiceName.Add(0, "Service 1");
     mv.Dic_NO_ServiceName.Add(1, "Service 2");
     mv.Dic_NO_ServiceName.Add(2, "Service 3");
     mv.Dic_NO_ServiceName.Add(3, "Service 4");
     mv.Dic_NO_ServiceName.Add(4, "Service 5");
     //add number
     mv.NS = 12.23456m;
     mv.NDCS_NO_VALUE.Add(0, 1.23456m);
     mv.NDCS_NO_VALUE.Add(1, 2.34567m);
     mv.NDCS_NO_VALUE.Add(2, 3.45678m);
     mv.NDCS_NO_VALUE.Add(3, 4.56789m);
     mv.NDCS_NO_VALUE.Add(4, 5.67890m);
     mv.NM = 23.45678m;
     mv.NIS_NO_VALUE.Add(0, 6.78901m);
     mv.NIS_NO_VALUE.Add(1, 7.89012m);
     mv.NIS_NO_VALUE.Add(2, 8.90123m);
     mv.NIS_NO_VALUE.Add(3, 9.01234m);
     mv.NIS_NO_VALUE.Add(4, 10.12345m);
     mv.NPI_NO_VALUE.Add(0, 11.23456m);
     mv.NPI_NO_VALUE.Add(1, 12.34567m);
     mv.NPI_NO_VALUE.Add(2, 13.45678m);
     mv.NPI_NO_VALUE.Add(3, 14.56789m);
     mv.NPI_NO_VALUE.Add(4, 15.67890m);
     mv.NII_NO_VALUE.Add(0, 16.78901m);
     mv.NII_NO_VALUE.Add(1, 17.89012m);
     mv.NII_NO_VALUE.Add(2, 18.90123m);
     mv.NII_NO_VALUE.Add(3, 19.01234m);
     mv.NII_NO_VALUE.Add(4, 20.12345m);
     mv.NMP_NO_VALUE.Add(0, 21.23456m);
     mv.NMP_NO_VALUE.Add(1, 22.34567m);
     mv.NMP_NO_VALUE.Add(2, 23.45678m);
     mv.NMP_NO_VALUE.Add(3, 24.56789m);
     mv.NMP_NO_VALUE.Add(4, 25.67890m);
     mv.WISL_NO_VALUE.Add(0, 1);
     mv.WISL_NO_VALUE.Add(1, 0);
     mv.WISL_NO_VALUE.Add(2, 1);
     mv.WISL_NO_VALUE.Add(3, 1);
     mv.WISL_NO_VALUE.Add(4, 0);
 }
        public void SetLevel2UnitValueTest()
        {
            //test Level 2
            Model      model = new Model();
            ModelValue mv    = new ModelValue();

            SetModelValueSample(mv);
            //calculate Level 3 values
            mv.CalculateLevel4Values();
            mv.CalculateLevel3Values();
            //create model
            model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = true;
            //model.TrimModel();
            model.CalculateModelWeight();
            mv.SetLevel4UnitValue(model.level4_nodes);
            mv.SetLevel3UnitValue(model.level3_nodes);
            mv.SetLevel2UnitValue(model.level2_nodes);
            //verify the value
            Assert.AreEqual(-0.5814090208767396425967497017122, (double)model.level2_nodes[0].value, 0.0001);
            Assert.AreEqual(4.5493525993415009929712588253279, (double)model.level2_nodes[1].value, 0.0001);
            Assert.AreEqual(4.435102352522840320814015528183, (double)model.level2_nodes[2].value, 0.0001);
            Assert.AreEqual(4.37469421809111477997698989807, (double)model.level2_nodes[3].value, 0.0001);

            ////修剪模型的情况
            //model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = false;
            //model.TrimModel();
            //model.CalculateModelWeight();
            //mv.SetLevel4UnitValue(model.level4_nodes);
            //mv.SetLevel3UnitValue(model.level3_nodes);
            //mv.SetLevel2UnitValue(model.level2_nodes);
            ////验证数值
            //Assert.AreEqual(-0.5814090208767396425967497017122, (double)model.level2_nodes[0].value, 0.0001);
            //Assert.AreEqual(4.5493525993415009929712588253279, (double)model.level2_nodes[1].value, 0.0001);
            //Assert.AreEqual(4.435102352522840320814015528183, (double)model.level2_nodes[2].value, 0.0001);
        }
Esempio n. 14
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        public override ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
        {
            if (TrainedModel == null)
            {
                throw new Exception("Must initialize the model before calling");
            }

            lock (TrainedModel)
            {
                var textLoader = GetTextLoader(Context, prediction);

                var pathToData = "";
                try
                {
                    // ugh have to spill data to disk for it to work!
                    pathToData = WriteToDisk(data, prediction);

                    IDataView dataView    = textLoader.Load(pathToData);
                    var       predictions = TrainedModel.Transform(dataView);
                    var       metrics     = Context.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");

                    return(new ModelFitness()
                    {
                        RMS = metrics.RootMeanSquaredError,
                        RSquared = metrics.RSquared
                    });
                }
                finally
                {
                    // cleanup
                    if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
                    {
                        File.Delete(pathToData);
                    }
                }
            }
        }
 private void SetDpItemValue(IWriteSession session, IDpItem dpItem, ModelValue value)
 {
     dpItem.Value = value;
     session.SaveHierarchy(dpItem);
 }
Esempio n. 16
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        public override ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
        {
            if (TrainedModel == null)
            {
                throw new Exception("Must initialize the model before calling");
            }
            if (data.Count == 0)
            {
                return(default(ModelFitness));
            }

            // values
            var predictions = new float[data.Count];
            var labels      = new float[data.Count];

            for (int i = 0; i < data.Count; i++)
            {
                predictions[i] = Predict(data[i]);
                switch (prediction)
                {
                case ModelValue.Action: labels[i] = (float)data[i].Action; break;

                case ModelValue.Angle: labels[i] = data[i].FaceAngle; break;

                case ModelValue.XY: labels[i] = data[i].MoveAngle; break;

                default: throw new Exception("Unknown prediction type : " + prediction);
                }
            }

            // calculate rms
            // https://sciencing.com/calculate-rms-5104500.html
            // rms(a,b,c) = sqrt((a^2 + b^2 + c^2)/3);
            var rms = 0d;

            for (int i = 0; i < predictions.Length; i++)
            {
                rms = Math.Pow(predictions[i] - labels[i], 2);
            }
            rms /= predictions.Length;
            rms  = Math.Sqrt(rms);

            // calculate r^2
            // https://www.sapling.com/5117357/calculate-rsquared
            // R = (Count (sum of ab) - (sum of a)(sum of b)) / [sqrt((Count(sum a^2) - (sum of a)^2)(Count *(sum of b^2) - (sum of b)^2)]
            //  a == labels
            //  b == prediction
            var ab = 0d;
            var a  = 0d;
            var b  = 0d;
            var a2 = 0d;
            var b2 = 0d;

            for (int i = 0; i < predictions.Length; i++)
            {
                a  += labels[i];
                b  += predictions[i];
                ab += (labels[i] * predictions[i]);
                a2 += Math.Pow(labels[i], 2);
                b2 += Math.Pow(predictions[i], 2);
            }
            var r2 = ((predictions.Length * ab) - (a * b)) / Math.Sqrt(((predictions.Length * a2) - Math.Pow(a, 2)) * (predictions.Length * b2 - Math.Pow(b, 2)));

            r2 = Math.Pow(r2, 2);

            return(new ModelFitness()
            {
                RMS = rms,
                RSquared = r2
            });
        }
        public void SetLevel3UnitValueTest()
        {
            Model      model = new Model();
            ModelValue mv    = new ModelValue();

            SetModelValueSample(mv);
            //calculate Level 3 values
            mv.CalculateLevel4Values();
            mv.CalculateLevel3Values();

            model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = true;
            //model.TrimModel();
            model.CalculateModelWeight();
            mv.SetLevel4UnitValue(model.level4_nodes);
            mv.SetLevel3UnitValue(model.level3_nodes);
            mv.SetLevel3UnitValue(model.level2_nodes);
            mv.SetLevel3UnitValue(model.level1_nodes);
            //verify the value of Level 3
            Assert.AreEqual(17.28380m / 12.23456m, model.level3_nodes[0].value);
            Assert.AreEqual(12.23456m / 23.45678m, model.level3_nodes[1].value);
            Assert.AreEqual(42.71615m / (12.23456m * 12.23456m - 12.23456m), model.level3_nodes[2].value);
            Assert.AreEqual((67.28380m + 92.71615m) / 12.23456m, model.level3_nodes[3].value);
            Assert.AreEqual(17.28380m / 12.23456m, model.level3_nodes[4].value);
            Assert.AreEqual(12.23456m / 23.45678m, model.level3_nodes[5].value);
            Assert.AreEqual((67.28380m * 67.28380m) / (23.45678m * 23.45678m), model.level3_nodes[6].value);
            Assert.AreEqual((117.28380m * 117.28380m) / (67.28380m * 67.28380m), model.level3_nodes[7].value);
            Assert.AreEqual(3 / 12.23456m, model.level3_nodes[8].value);
            Assert.AreEqual((117.28380m * 117.28380m) / (67.28380m * 67.28380m), model.level3_nodes[9].value);
            Assert.AreEqual(3 / 12.23456m, model.level3_nodes[10].value);
            Assert.AreEqual(12.23456m / 23.45678m, model.level3_nodes[11].value);
            Assert.AreEqual((67.28380m * 67.28380m) / (23.45678m * 23.45678m), model.level3_nodes[12].value);
            //verify the value of Level 2
            foreach (var node in model.level2_nodes)
            {
                Assert.AreEqual(0, node.value);
            }
            //verify the value of Level 1
            foreach (var node in model.level1_nodes)
            {
                Assert.AreEqual(0, node.value);
            }

            ////裁剪模型的情况
            //model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = false;
            //model.TrimModel();
            //model.CalculateModelWeight();
            //mv.SetLevel4UnitValue(model.level4_nodes);
            //mv.SetLevel3UnitValue(model.level3_nodes);
            //mv.SetLevel3UnitValue(model.level2_nodes);
            //mv.SetLevel3UnitValue(model.level1_nodes);
            ////验证第3层的值
            //Assert.AreEqual(17.28380m / 12.23456m, model.level3_nodes[0].value);
            //Assert.AreEqual(12.23456m / 23.45678m, model.level3_nodes[1].value);
            //Assert.AreEqual(42.71615m / (12.23456m * 12.23456m - 12.23456m), model.level3_nodes[2].value);
            //Assert.AreEqual((67.28380m + 92.71615m) / 12.23456m, model.level3_nodes[3].value);
            //Assert.AreEqual(17.28380m / 12.23456m, model.level3_nodes[4].value);
            //Assert.AreEqual(12.23456m / 23.45678m, model.level3_nodes[5].value);
            //Assert.AreEqual((67.28380m * 67.28380m) / (23.45678m * 23.45678m), model.level3_nodes[6].value);
            //Assert.AreEqual((117.28380m * 117.28380m) / (67.28380m * 67.28380m), model.level3_nodes[7].value);
            //Assert.AreEqual(3 / 12.23456m, model.level3_nodes[8].value);
            //Assert.AreEqual((117.28380m * 117.28380m) / (67.28380m * 67.28380m), model.level3_nodes[9].value);
            //Assert.AreEqual(3 / 12.23456m, model.level3_nodes[10].value);
            ////验证第2层的值
            //foreach (var node in model.level2_nodes)
            //    Assert.AreEqual(0, node.value);
            ////验证第1层的值
            //foreach (var node in model.level1_nodes)
            //    Assert.AreEqual(0, node.value);
        }
Esempio n. 18
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 public virtual ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
 {
     return(default(ModelFitness));
 }
Esempio n. 19
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 // train
 public Model(IEnumerable <ModelDataSet> data, ModelValue prediction)
 {
 }
Esempio n. 20
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        // train
        public ModelMLNet(IEnumerable <ModelDataSet> data, ModelValue prediction)
        {
#if ML_LEGACY
            var pipeline = new LearningPipeline();

            // add data
            pipeline.Add(CollectionDataSource.Create(data));

            // choose what to predict
            switch (prediction)
            {
            case ModelValue.Action:
                pipeline.Add(new ColumnCopier(("Action", "Label")));
                break;

            case ModelValue.Angle:
                pipeline.Add(new ColumnCopier(("FaceAngle", "Label")));
                break;

            case ModelValue.XY:
                pipeline.Add(new ColumnCopier(("MoveAngle", "Label")));
                break;

            default: throw new Exception("Unknown prediction : " + prediction);
            }

            // add columns as features
            // do not include the features which should be predicted
            pipeline.Add(new ColumnConcatenator("Features",
                                                "CenterAngle",
                                                "InZone",
                                                "Health",
                                                "Shield",
                                                "Primary",
                                                "PrimaryAmmo",
                                                "PrimaryClip",
                                                "Secondary",
                                                "SecondaryAmmo",
                                                "SecondaryClip",
                                                // "Ammo",
                                                "Angle_1",
                                                "Distance_1",
                                                // "Bandage",
                                                "Angle_2",
                                                "Distance_2",
                                                // "Helmet",
                                                "Angle_3",
                                                "Distance_3",
                                                // "Ak47",
                                                "Angle_4",
                                                "Distance_4",
                                                // "Shotgun",
                                                "Angle_5",
                                                "Distance_5",
                                                // "Pistol",
                                                "Angle_6",
                                                "Distance_6",
                                                // "Obstacle",
                                                "Angle_7",
                                                "Distance_7",
                                                // "Player",
                                                "Angle_8",
                                                "Distance_8"
                                                ));

            // add a classifier
            // action 0.25, xy 84.9777, angle 71.591
            //pipeline.Add(new FastTreeRegressor());

            pipeline.Add(new FastTreeRegressor()
            {
                // NumTrees
                //  100 - default
                //  100 - xy 79
                // 1000 - xy 63, angle 52 (slow)
                NumTrees = 100,
                // NumLeaves
                //   50 - default
                //  100 - xy 63, angle 71
                // 1000 - xy 59, angle 49 (slow)
                NumLeaves = 50,
                // NumThreads
                // 5 - default
                NumThreads = 50,
                // EntropyCoe
                // 0.30 - default
                // 0.70 - xy 79, angle 66
                // 0.99 - xy 79, angle 66
                // 0.05 - xy 79, angle 66
                EntropyCoefficient = 0.3
            });

            // action 0.26 xy 85.1606, angle 72.5194
            //pipeline.Add(new FastTreeTweedieRegressor());
            // took too long
            //pipeline.Add(new GeneralizedAdditiveModelRegressor());
            // runtime exception
            //pipeline.Add(new LightGbmRegressor());
            // action 0.4736, xy 105.8815, angle 91.2677
            //pipeline.Add(new OnlineGradientDescentRegressor());
            // runtime exception
            //pipeline.Add(new OrdinaryLeastSquaresRegressor());
            // action 0.4628, xy 106.0547, angle 91.0179
            //pipeline.Add(new PoissonRegressor());
            // action 0.4599, xy 105.8474, angle 90.4134
            //pipeline.Add(new StochasticDualCoordinateAscentRegressor());

            // train the model
            TrainedModel = pipeline.Train <ModelDataSet, ModelDataSetPrediction>();
#else
            Context = new MLContext();

            // add data
            var textLoader = GetTextLoader(Context, prediction);

            // spill to disk !?!?! since there is no way to load from a collection
            var pathToData = "";
            try
            {
                // write data to disk
                pathToData = WriteToDisk(data, prediction);

                // read in data
                IDataView dataView = textLoader.Read(pathToData);

                // configurations
                var label = "";
                switch (prediction)
                {
                case ModelValue.Action: label = "Action"; break;

                case ModelValue.Angle: label = "FaceAngle"; break;

                case ModelValue.XY: label = "MoveAngle"; break;

                default: throw new Exception("Unknown value for prediction : " + prediction);
                }
                var dataPipeline = Context.Transforms.CopyColumns(label, "Label")
                                   .Append(Context.Transforms.Concatenate("Features", ColumnNames(false /* do not include the label */, prediction)));

                // set the training algorithm
                var trainer          = Context.Regression.Trainers.FastTree(label: "Label", features: "Features");
                var trainingPipeline = dataPipeline.Append(trainer);

                TrainedModel = trainingPipeline.Fit(dataView);
            }
            finally
            {
                // cleanup
                if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
                {
                    File.Delete(pathToData);
                }
            }
#endif
        }
        public void SetLevel4UnitValueTest()
        {
            Model      model = new Model();
            ModelValue mv    = new ModelValue();

            SetModelValueSample(mv);
            //calculate Level 4 values
            mv.CalculateLevel4Values();

            model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = true;
            //model.TrimModel();
            model.CalculateModelWeight();
            mv.SetLevel4UnitValue(model.level4_nodes);
            mv.SetLevel4UnitValue(model.level3_nodes);
            mv.SetLevel4UnitValue(model.level2_nodes);
            mv.SetLevel4UnitValue(model.level1_nodes);
            //verify the value of Level 4
            Assert.AreEqual(12.23456m, model.level4_nodes[0].value);
            Assert.AreEqual(17.28380m, model.level4_nodes[1].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[2].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[3].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[4].value);
            Assert.AreEqual(42.71615m, model.level4_nodes[5].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[6].value);
            Assert.AreEqual(67.28380m, model.level4_nodes[7].value);
            Assert.AreEqual(92.71615m, model.level4_nodes[8].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[9].value);
            Assert.AreEqual(17.28380m, model.level4_nodes[10].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[11].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[12].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[13].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[14].value);
            Assert.AreEqual(67.28380m, model.level4_nodes[15].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[16].value);
            Assert.AreEqual(117.28380m, model.level4_nodes[17].value);
            Assert.AreEqual(67.28380m, model.level4_nodes[18].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[19].value);
            Assert.AreEqual(3, model.level4_nodes[20].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[21].value);
            Assert.AreEqual(117.28380m, model.level4_nodes[22].value);
            Assert.AreEqual(67.28380m, model.level4_nodes[23].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[24].value);
            Assert.AreEqual(3, model.level4_nodes[25].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[26].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[27].value);
            Assert.AreEqual(12.23456m, model.level4_nodes[28].value);
            Assert.AreEqual(23.45678m, model.level4_nodes[29].value);
            Assert.AreEqual(67.28380m, model.level4_nodes[30].value);
            //verify the value of Level 3
            foreach (var node in model.level3_nodes)
            {
                Assert.AreEqual(0, node.value);
            }
            //verify the value of Level 2
            foreach (var node in model.level2_nodes)
            {
                Assert.AreEqual(0, node.value);
            }
            //verify the value of Level 1
            foreach (var node in model.level1_nodes)
            {
                Assert.AreEqual(0, node.value);
            }

            ////裁剪模型的情况
            //model.CreateModel();
            //GlobalData.GlobalData.globalData.if_assess_modularity = false;
            //model.TrimModel();
            //model.CalculateModelWeight();
            //mv.SetLevel4UnitValue(model.level4_nodes);
            //mv.SetLevel4UnitValue(model.level3_nodes);
            //mv.SetLevel4UnitValue(model.level2_nodes);
            //mv.SetLevel4UnitValue(model.level1_nodes);
            ////验证第4层的值
            //Assert.AreEqual(12.23456m, model.level4_nodes[0].value);
            //Assert.AreEqual(17.28380m, model.level4_nodes[1].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[2].value);
            //Assert.AreEqual(23.45678m, model.level4_nodes[3].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[4].value);
            //Assert.AreEqual(42.71615m, model.level4_nodes[5].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[6].value);
            //Assert.AreEqual(67.28380m, model.level4_nodes[7].value);
            //Assert.AreEqual(92.71615m, model.level4_nodes[8].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[9].value);
            //Assert.AreEqual(17.28380m, model.level4_nodes[10].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[11].value);
            //Assert.AreEqual(23.45678m, model.level4_nodes[12].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[13].value);
            //Assert.AreEqual(23.45678m, model.level4_nodes[14].value);
            //Assert.AreEqual(67.28380m, model.level4_nodes[15].value);
            //Assert.AreEqual(23.45678m, model.level4_nodes[16].value);
            //Assert.AreEqual(117.28380m, model.level4_nodes[17].value);
            //Assert.AreEqual(67.28380m, model.level4_nodes[18].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[19].value);
            //Assert.AreEqual(3, model.level4_nodes[20].value);
            //Assert.AreEqual(23.45678m, model.level4_nodes[21].value);
            //Assert.AreEqual(117.28380m, model.level4_nodes[22].value);
            //Assert.AreEqual(67.28380m, model.level4_nodes[23].value);
            //Assert.AreEqual(12.23456m, model.level4_nodes[24].value);
            //Assert.AreEqual(3, model.level4_nodes[25].value);
            ////验证第3层的值
            //foreach (var node in model.level3_nodes)
            //    Assert.AreEqual(0, node.value);
            ////验证第2层的值
            //foreach (var node in model.level2_nodes)
            //    Assert.AreEqual(0, node.value);
            ////验证第1层的值
            //foreach (var node in model.level1_nodes)
            //    Assert.AreEqual(0, node.value);
        }
Esempio n. 22
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 public Value(ModelValue model, double value = 0)
 {
     this.model = model;
     this.value = value;
 }
Esempio n. 23
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 public string GetRawModelXML(ModelValue model)
 {
     return((model?.NameVersion?.CCacheId as NodeSetFileModel)?.FileCache);
 }
Esempio n. 24
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		protected ModelValue			DetermineParameterValue(ModelParameter parameter)
		{
			ModelValues values = new ModelValues();
			values.Add(parameter.Values);

			//Parameter values, can be specified in numerous ways:
			//	1.  Value list - simply choose one of them
			//	2.  Expression - generate value that meets the criteria (ie: <, >, !=, etc)
			//	3.  Variable - simply obtain the value by calling a method/field

			//#3. Variable - simply obtain the value by calling a method/field
			if(parameter.Variable != null)
			{
                object current = parameter.Variable.CachedValue;
				if(current is IEnumerable && !typeof(IEnumerable).IsAssignableFrom(parameter.Type))
				{
 					foreach(object v in (IEnumerable)current)
						values.Add(new ModelValue(v));
				}
				else
				{
					values.Add(new ModelValue(current));
				}
			}

			//First ensure we have a set of values/requirements to choose from
			if(values.Count <= 0)
				throw new ModelException(parameter, "No values specified to choose from");

			//Note: Since we allow the operator on the individual values, this is a little more complex.
			//Note: We allow multiple operators, not just one, (ie: x > 5, < 10, and != 7). 
			//This gives you great power and flexibility in expressions, but means we have to work a 
			//little hard in determing a value that meets the requirements

			//#1.  Value list - simply choose one of them
			//Note: Bitmask is already exploded into combinations
			ModelValues equalvalues = values.FindOperator(ModelValueOperator.Equal);

			//#2.  Expression - generate value that meets the criteria (ie: <, >, !=, etc)
			//Note: Since we allow the operator on the individual values, this is a little more complex.
			//Note: We allow multiple operators, not just one, (ie: x > 5, < 10, and != 7). 
			//This gives you great power and flexibility in expressions, but means we have to work a 
			//little hard in determing a value that meets the requirements.
			int min = Int32.MinValue;
			int max = Int32.MaxValue;

			//Adjust our parameter, simplier to loop over all of them
			foreach(ModelValue value in values.FindOperator(ModelValueOperator.Equal, false).FindOperator(ModelValueOperator.NotEqual, false))
			{
				//To keep this simple, we just support integers (for now).
				if(!(value.Value is int || value.Value is Nullable<int>))
					throw new ModelException(parameter, "Generated value range must be specified in terms of integers, not '" + value.Type + "'");

				//Simplify our life
				int v = (int)value.Value;
				
				//Adjust Max (if there is one)
				switch(value.Operator)
				{
					case ModelValueOperator.LessThanOrEqual:
						if(v < max)
							max = v;
						break;
				
					case ModelValueOperator.GreaterThanOrEqual:
						if(v > min)
							min = v;
						break;

					case ModelValueOperator.LessThan:
						if(v-1 < max && v > Int32.MinValue/*prevent underflow*/)
							max = v-1;
						break;
				
					case ModelValueOperator.GreaterThan:
						if(v+1 > min && v < Int32.MaxValue/*prevent overflow*/)
							min = v+1;
						break;
				};
			}

			//Choose a new value, within the specified range
			//Note: We retry in case it equals one of the existing invalid values (ie: !=)
			while(true)
			{
				ModelValue choice = null;
				if(equalvalues.Count > 0)
				{
					//Simple: Choose one of the specified values
					choice = new ModelValue(equalvalues.Choose(this).Value);
				}
				else
				{
					//Otherwise: Choose a value within in the range
					//Note: Random.Next = min <= x < max (so we have to add one)
					int index = _options.Random.Next(min, max < Int32.MaxValue ? max + 1 : max);	//Prevent overflow
                    choice = new ModelValue(index);
				}

				//As soon as we find a value, within the range, (and not in the invalid list), were done
				bool valid = true;
				foreach(ModelValue invalid in values.FindOperator(ModelValueOperator.NotEqual))
				{
					if(invalid.Evaluate(choice.Value, ModelValueOperator.Equal))
						valid = false;
				}

				if(valid)
					return choice;
			}
		}
Esempio n. 25
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        // train
        public ModelOpenCV(List <ModelDataSet> input, ModelValue prediction)
        {
            if (input == null || input.Count == 0)
            {
                throw new Exception("Must have valid input");
            }

            // convert features into proper form
            var features = new float[input.Count, input[0].Features()];
            var labels   = new float[input.Count];

            for (int i = 0; i < input.Count; i++)
            {
                for (int j = 0; j < input[i].Features(); j++)
                {
                    features[i, j] = input[i].Feature(j);
                }

                switch (prediction)
                {
                case ModelValue.Action: labels[i] = input[i].Action; break;

                case ModelValue.Angle: labels[i] = input[i].FaceAngle; break;

                case ModelValue.XY: labels[i] = input[i].MoveAngle; break;

                default: throw new Exception("Unknown prediction type : " + prediction);
                }
            }

            // train
            var labelInput = InputArray.Create <float>(labels);
            var dataInput  = InputArray.Create <float>(features);

            TrainedModel = RTrees.Create();
            TrainedModel.RegressionAccuracy = 0.00001f;
            // RTrees.MaxDepath (r^2)
            //  default - action 0.3424, xy 0.1735, angle 0.2208
            //    5     -
            //   20     - action 0.6482, xy 0.5912, angle 0.6414 (new default)
            //  100     - action 0.6408, xy 0.5914, angle 0.6419
            TrainedModel.MaxDepth = 20;
            // RTress.MinSampleCount
            //  default(10) - see 20 above
            //     1        - actions 0.6625, xy 0.5077, angle 0.6376
            //    50        - actions 0.6464, xy 0.5627, angle 0.6217
            //TrainedModel.MinSampleCount = 1;

            // fails
            //TrainedModel = LogisticRegression.Create();

            //  fails
            // TrainedModel = DTrees.Create();

            // failed
            //TrainedModel = SVM.Create();
            //TrainedModel.KernelType = SVM.KernelTypes.Linear;
            //TrainedModel.Type = SVM.Types.NuSvr;
            //TrainedModel.C = 1;
            //TrainedModel.P = 0.01;
            //TrainedModel.Gamma = 10f;
            //TrainedModel.Degree = 0.1;
            //TrainedModel.Coef0 = 0;
            //TrainedModel.Nu = 0.1;

            TrainedModel.Train(dataInput, SampleTypes.RowSample, labelInput);
        }
Esempio n. 26
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 //Constructor
 public ModelExpression(ModelFunction func, ModelValue value)
     : base(null, value)
 {
     _func = func;
 }
    public void LearnPS(int N, int episodes)
    {
        subgoalVar = new List <double>();
        forceStop  = false;
        //int stateHold = currentState;

        float alpha = 0.2f, gamma = 0.95f, theta = 0.001f;
        int   s = currentState; //Initialize

        List <PriorityObj> PQueue = new List <PriorityObj>();

        visit = new Dictionary <int, int>();
        int epsilon_counter = 0;
        int steps           = 0;

        while (true)
        {
            if (forceStop)
            {
                break;
            }

            if (epsilon_counter++ % 100 == 0)
            {
                epsilon_counter = 1;
                e = 0.01 + 0.7 * Math.Pow(Math.E, -(discount++) / 20.0);
            }

            int tempSteps = steps; //Number of steps per episode

            if (EpisodeSteps.Count > 1500)
            {
                forceStop = true;
            }

            //(b) a <- policy(s,Q)
            //int a = e_greedy(Q[s]);
            int a = softmaxWVisits(Q[s]);
            // Q[s][a] -= -0.001;
            double tempVal = Q[s][a];
            //tempFirstVisitHist[s[0], s[1]] += 1;
            //(c) Execute action a
            int     sP      = 0;
            double  r       = 0;
            Context curCtxt = currentContext;
            lock (threadLock)
            {
                executeAction(s, a, ref sP, ref r, ref steps);
            }
            if (!visit.ContainsKey(sP))
            {
                visit.Add(sP, 0);
            }
            Context nexCtxt = currentContext;
            if (curCtxt != nexCtxt)
            {
                //double tmp = curCtxt.Q[s][Q[s].GetMaxAction()];
                //for (int i = 0; i < curCtxt.Q[s].Actions.Count; i++)
                //{
                //    Q[s][i] += curCtxt.Q[s][i];//tmp / 8;
                //}
                continue;
            }
            //SwitchContext(curCtxt);
            int result = sP;

            //(d) Model(s,a) <- s', r
            //Model[s[0], s[1], a] = new int[] { sP[0], sP[1], r, steps - tempSteps };

            if (Model[s, a] == null)
            {
                ModelValue m = new ModelValue(s, a, sP, r, steps - tempSteps);
                Model[s, a] = m;
            }
            else
            if (Model[s, a].calculateEm(sP, r) > 0)
            {
                Model[s, a].Update(s, a, sP, r, steps - tempSteps);
            }
            //System.Diagnostics.Debug.WriteLine("\tContext: " + currentContext.cID + "\t(s,a,sp): " + s + ", " + a + ", " + sP + "\tTm0: " /*+ (Model[s, a].Tm.Count > 0 ? Model[s,a].Tm[0].ToString() : " ") */ + "\tem0: " + em + "\tEm0: " + contexts[0].Em + "\tEm1: " + (contexts.Count > 1 ? contexts[1].Em.ToString() : "-"));

            //(e) p <- |r + gama*maxA'Q(s',a') - Q(s,a)|
            //float p = Math.Abs(r + gamma * getQ(sP[0], sP[1], maxA(sP)) - getQ(s[0], s[1], a));
            double p = Math.Abs(r + gamma * Q[sP][Q[sP].GetMaxAction()] - Q[s][a]);

            //(f) if p > tetha, then insert s,a into PQueue with priority p
            PQueue.Clear();
            if (p > theta)
            {
                InsertQueue(PQueue, s, a, p);
            }

            //(g) Repeat N times while PQueue is not empty
            for (int i = 0; i < N && 0 < PQueue.Count; i++)
            {
                //(-)s, a <- first(PQueue)
                PriorityObj obj = PQueue[0];// dene!!!!!!!!!!!!11
                PQueue.Remove(obj);
                s = obj.State;
                a = obj.Action;

                //(-)s', r <- Modell(s,a)
                sP = Model[s, a].sP;
                r  = Model[s, a].reward;
                int tsteps = Model[s, a].steps;

                //(-)Q(s, a) <- Q(s,a) + alpha[r + gama*maxA'Q(s',a') - Q(s,a)]
                //e_updates.Add(new double[] { s, a, Q[s][a] });
                Q[s][a] = Q[s][a] + alpha * (r + Math.Pow(gamma, tsteps) * Q[sP][Q[sP].GetMaxAction()] - Q[s][a]);

                //float t = (float)(getQ(s[0], s[1], a) + alpha * (r + Math.Pow(gamma, tsteps) * getQ(sP[0], sP[1], maxA(sP)) - getQ(s[0], s[1], a)));
                //setQ(s[0], s[1], a, t);

                //(-)Repeat, for all s",a" predicted to lead to s
                List <ModelValue> list = Model.All_SA_PredictedToLeadTo(s);
                for (int j = 0; j < list.Count; j++)
                {
                    int sDP = list[j].s;
                    int aDP = list[j].a;

                    // r" <- predicted reward
                    double rDP = list[j].reward;

                    // p <- |r" + gamma*maxaQ(s,a) - Q(s",a")|
                    p = Math.Abs(rDP + gamma * Q[s][Q[s].GetMaxAction()] - Q[sDP][aDP]);

                    // if p > tetha, then insert s",a" into PQueue with priority p
                    if (p > theta)
                    {
                        InsertQueue(PQueue, sDP, aDP, p);
                    }
                }
            }
            s = result;
            environment.State = s;
            if (environment.isGoal(s))
            {
                //visit = new Dictionary<int, int>();///////////
                //if (subgoalValues != null)
                //{
                //    subgoalValues[s] = 0.1;
                //    for (int f = 0; f < 4; f++)
                //    {
                //        if (Model.StateCount < s)
                //            Model.States.Add(new ModelState(s));
                //        if (Model[s] == null)
                //            Model[s] = new ModelState(s);
                //        if(Model[s].Count <= 4)
                //            Model[s].Add(new ModelValue(s, f, s, 0, 0));
                //    }
                //}
                if (Model.States.Count <= s)
                {
                    Model[s, a]     = null;
                    Model.States[s] = new ModelState(s);
                }
                //e = 0.9 * Math.Pow(Math.E, -(discount++) / 50.0); //Update epsilon
                if (!DisableOptions && !m_optsLearned)
                {
                    //initiationSetMembers = new int[Model.StateCount];
                    //initiationSets = new List<InitiationSet>();
                    Subgoals = SearchSubgoals();
                    //Subgoals.Add(new SubGoal(s));
                    SubgoalCounts.Add(Subgoals.Count);
                    double mean = 0;
                    double std  = 100;
                    int    sc   = 10;
                    if (SubgoalCounts.Count > sc)
                    {
                        std = 0;
                        for (int c = SubgoalCounts.Count - sc; c < SubgoalCounts.Count; c++)
                        {
                            mean += SubgoalCounts[c] / (double)sc;
                        }
                        for (int c = SubgoalCounts.Count - sc; c < SubgoalCounts.Count; c++)
                        {
                            std += Math.Pow(SubgoalCounts[c] - mean, 2) / sc;
                        }
                        subgoalVar.Add(std);
                        std = Math.Sqrt(std);
                    }
                    else
                    {
                        subgoalVar.Add(-1);
                    }


                    if (std < 0.01)
                    {
                        List <OptionN> options = createOptFromSubGoals(Subgoals);
                        currentContext.options = options;
                        optNonLearnedVals      = 0;
                        optLearnedVals         = 0;
                        for (int i = 0; i < Model.StateCount; i++)
                        {
                            if (Model[i] == null)
                            {
                                optNonLearnedVals += 4;
                            }
                            else
                            {
                                for (int j = 0; j < Model[i].Count; j++)
                                {
                                    if (Model[i][j] == null || Q[i][j] == 0)
                                    {
                                        optNonLearnedVals += 1;
                                    }
                                    else
                                    {
                                        optLearnedVals += 1;
                                    }
                                }
                            }
                        }
                        //SearchInitiationSets(Subgoals); //Bu satır if in içinde olmalı (ki öyle(değil mi?))
                        //List<Option> options = CreateOptions();
                        OptionLearn(options);
                        //currentContext.options = options;
                    }
                }

                s = currentState;
                EpisodeSteps.Add(steps);
                ContextTrack.Add(currentContext.cID);
                currentContext.EpisodeSteps.Add(steps);
                steps = 0;
                int calcLength = 9;
                if (currentContext.EpisodeSteps.Count > calcLength)
                {
                    double mean = 0;
                    double std  = 0;
                    for (int c = currentContext.EpisodeSteps.Count - calcLength; c < currentContext.EpisodeSteps.Count; c++)
                    {
                        mean += EpisodeSteps[c] / (double)calcLength;
                    }
                    for (int c = currentContext.EpisodeSteps.Count - calcLength; c < currentContext.EpisodeSteps.Count; c++)
                    {
                        std += Math.Pow(currentContext.EpisodeSteps[c] - mean, 2) / calcLength;
                    }
                    std = Math.Sqrt(std);
                    currentContext.std = std;

                    bool stop = true;
                    for (int cx = 0; cx < contexts.Count; cx++)
                    {
                        if (contexts[cx].std > 10)
                        {
                            stop = false;
                        }
                        //System.Diagnostics.Debug.Write(cx + ":" + contexts[cx].std + "   ");
                    }
                    //System.Diagnostics.Debug.Write("\n");
                    if (stopByEpisode)
                    {
                        if (stopEpisode <= EpisodeSteps.Count)
                        {
                            break;
                        }
                    }
                    else
                    {
                        if (stop || forceStop)
                        {
                            break;
                        }
                    }
                }//Varyans ile dene

                //if (!m_optsLearned)
                //{
                //    OptionSearch();
                //    OptionLearn();
                //}
                PQueue.Clear();
                if (curCtxt != nexCtxt)
                {
                    SwitchContext(nexCtxt);
                }
                if (oneStepLearn)
                {
                    break;
                }
                e_updates = new List <double[]>();
            }
        }
        environment.State = currentState;
    }
Esempio n. 28
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 public DataAnalyze(JSONDataStruct.Rootobject datasource, ModelValue mv)
 {
     data       = datasource;
     modelValue = mv;
 }
Esempio n. 29
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 public static Value Max(ModelValue model)
 {
     return(new Value(model, model.Max));
 }
Esempio n. 30
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 public void Add(ModelValue value)
 {
     modelValues.Add(value);
 }