public static Parse <T> CreateValue <T>(this Parse <T> parse, NetMLObjectBuilder netMLObjectBuilder)
{
return(value =>
{
ParseResult <T> result = parse(value);
if (result.Succeeded)
{
double decValue;
var culture = new CultureInfo("en-gb");
bool boolValue;
if (double.TryParse(result.Result.ToString(), NumberStyles.Number, culture, out decValue))
{
netMLObjectBuilder.AddValue(decValue);
}
else if (bool.TryParse(result.Result.ToString(), out boolValue))
{
netMLObjectBuilder.AddValue(boolValue);
}
else
{
var str =
result.Result.ToString().Replace("'", "").ToString();
netMLObjectBuilder.AddValue(str);
}
}
return result;
});
}
public NetMLObject Parse(string netMLString)
{
var netMLObjectBuilder = new NetMLObjectBuilder();
var netMLObject = ParserObject(netMLObjectBuilder);
var result = netMLObject(netMLString);
return(netMLObjectBuilder.MainNetMLObject);
}
private Parse <string> CreateLogisticregression(NetMLObjectBuilder netMLObjectBuilder)
{
var logitcostfunction = Parser.Literal("logitcostfunction").CreateOption(netMLObjectBuilder);
var tanhcostfunction = Parser.Literal("tanhcostfunction").CreateOption(netMLObjectBuilder);
var logisticregression = Parser.Literal("logisticregression").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => logitcostfunction.Or(tanhcostfunction));
return(logisticregression);
}
private Parse <string> CreateDecisiontree(NetMLObjectBuilder netMLObjectBuilder)
{
var decisiontreeshannonentropysplitter = Parser.Literal("shannonentropysplitter").CreateOption(netMLObjectBuilder);
var decisiontreeginiindexsplitter = Parser.Literal("giniindexsplitter").CreateOption(netMLObjectBuilder);
var createdecisiontree = Parser.Literal("decisiontree").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => decisiontreeshannonentropysplitter.Or(decisiontreeginiindexsplitter)); //.Or(Parser.Literal("")
return(createdecisiontree);
}
private Parse <string> CreateNaivebayers(NetMLObjectBuilder netMLObjectBuilder)
{
var linearbayeskernel = Parser.Literal("linearbayeskernel").CreateOption(netMLObjectBuilder);
var gaussianbayeskernel = Parser.Literal("gaussianbayeskernel").CreateOption(netMLObjectBuilder);
var createnaivebayers = Parser.Literal("naivebayers").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => linearbayeskernel.Or(gaussianbayeskernel));
return(createnaivebayers);
}
public static Parse <T> CreateNetMLObject <T>(this Parse <T> parse,
NetMLObjectBuilder netMLObjectBuilder)
{
return(value =>
{
ParseResult <T> result = parse(value);
if (result.Succeeded)
{
netMLObjectBuilder.Create(new NetMLObject());
}
return result;
});
}
public static Parse <string> CreateAlgorithmus(this Parse <string> parse, NetMLObjectBuilder netMLObjectBuilder)
{
return(value =>
{
ParseResult <string> result = parse(value);
if (result.Succeeded)
{
var culture = new CultureInfo("en-gb");
var str =
result.Result.ToString();
netMLObjectBuilder.CreateAlgorithmus(str);
}
return result;
});
}
private Parse <string> CreateKNN(NetMLObjectBuilder netMLObjectBuilder)
{
var knneuclidmetric = Parser.Literal("euclidmetric").CreateOption(netMLObjectBuilder);
var knnmanhattanmetric = Parser.Literal("manhattanmetric").CreateOption(netMLObjectBuilder);
var knnmaximummetric = Parser.Literal("maximummetric").CreateOption(netMLObjectBuilder);
var knnsquardeuclidmetric = Parser.Literal("squaredeuclidmetric").CreateOption(netMLObjectBuilder);
var createKNN = Parser.Literal("knn").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => knneuclidmetric.Or(knnmanhattanmetric).Or(knnmaximummetric).Or(knnsquardeuclidmetric));
return(createKNN);
}
public static Parse <T> CreateVariable <T>(this Parse <T> parse, NetMLObjectBuilder netMLObjectBuilder)
{
return(value =>
{
ParseResult <T> result = parse(value);
if (result.Succeeded)
{
var culture = new CultureInfo("en-gb");
var str =
result.Result.ToString();
netMLObjectBuilder.AddVariable(str);
}
return result;
});
}
private Parse <string> CreateRandomforest(NetMLObjectBuilder netMLObjectBuilder)
{
var decisiontreeshannonentropysplitter = Parser.Literal("shannonentropysplitter").CreateOption(netMLObjectBuilder);
var decisiontreeginiindexsplitter = Parser.Literal("giniindexsplitter").CreateOption(netMLObjectBuilder);
var bagging = Parser.Literal("bagging").CreateOption(netMLObjectBuilder);
var boosting = Parser.Literal("boosting").CreateOption(netMLObjectBuilder);
var randomforestOption = bagging.Or(boosting);
var randomforest = Parser.Literal("Randomforest").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => decisiontreeshannonentropysplitter.Or(decisiontreeginiindexsplitter)).Then(_x => randomforestOption);
return(randomforest);
}
private Parse <string> CreateClustering(NetMLObjectBuilder netMLObjectBuilder)
{
var knneuclidmetric = Parser.Literal("euclidmetric").CreateOption(netMLObjectBuilder);
var knnmanhattanmetric = Parser.Literal("manhattanmetric").CreateOption(netMLObjectBuilder);
var knnmaximummetric = Parser.Literal("maximummetric").CreateOption(netMLObjectBuilder);
var knnsquardeuclidmetric = Parser.Literal("squaredeuclidmetric").CreateOption(netMLObjectBuilder);
var KMeans = CreateKMeans(netMLObjectBuilder);
var KMetroids = CreateKmetroids(netMLObjectBuilder);
var createClustering = Parser.Literal("clustering").CreateClassificationAlgorithmusObject(netMLObjectBuilder)
.Then(_ => KMeans.Or(KMetroids).Then(__ => knneuclidmetric.Or(knnmanhattanmetric).Or(knnmaximummetric).Or(knnsquardeuclidmetric)));
return(createClustering);
}
private void CreateSupportVectorMachine(NetMLObjectBuilder netMLObjectBuilder,
out Parse <string> supportvectormachine, out Parse <string> dualperceptron)
{
var linearkernel = Parser.Literal("linearkernel").CreateOption(netMLObjectBuilder);
var gaussiankernel = Parser.Literal("gaussiankernel").CreateOption(netMLObjectBuilder);
var polynomialkernel = Parser.Literal("polynomialkernel").CreateOption(netMLObjectBuilder);
var logitkernel = Parser.Literal("logitkernel").CreateOption(netMLObjectBuilder);
var tanhkernel = Parser.Literal("tanhkernel").CreateOption(netMLObjectBuilder);
var n = Parser.Literal("n").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.DecimalString().CreateValue(netMLObjectBuilder)));
var c = Parser.Literal("c").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.DecimalString().CreateValue(netMLObjectBuilder)));
var kernel = linearkernel.Or(gaussiankernel).Or(polynomialkernel).Or(logitkernel).Or(tanhkernel);
dualperceptron = Parser.Literal("dualperceptron").CreateAlgorithmus(netMLObjectBuilder).Then(_ =>
kernel);
supportvectormachine = Parser.Literal("supportvectormachine").CreateAlgorithmus(netMLObjectBuilder).Then(_ =>
kernel).Then(_x => n).Then(_xx => c);
}
private void NeuronalNetworks(NetMLObjectBuilder netMLObjectBuilder,
out Parse <string> backpropagation, out Parse <string> radialbasisfunction, out Parse <string> selforganisingmap)
{
var inputneurons = Parser.Literal("inputneurons").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.IntegerString().CreateValue(netMLObjectBuilder)));
var outputneurons = Parser.Literal("outputneurons").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.IntegerString().CreateValue(netMLObjectBuilder)));
var firsthiddenlayerneurons = Parser.Literal("firsthiddenlayerneurons").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.IntegerString().CreateValue(netMLObjectBuilder)));
var secondhiddenlayerneurons = Parser.Literal("secondhiddenlayerneurons").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.IntegerString().CreateValue(netMLObjectBuilder)));
var evolutions = Parser.Literal("evolutions").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.IntegerString().CreateValue(netMLObjectBuilder)));
var learningrate = Parser.Literal("learningrate").CreateVariable(netMLObjectBuilder).
Then(_ => Parser.Literal("=").And(Parser.DecimalString().CreateValue(netMLObjectBuilder)));
backpropagation = Parser.Literal("backpropagation").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => inputneurons.And(outputneurons).
Then(_x => firsthiddenlayerneurons).
Then(_xx => secondhiddenlayerneurons).
Then(_xxx => evolutions).
Then(_xxxx => learningrate));
radialbasisfunction = Parser.Literal("radialbasisfunction").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => inputneurons.And(outputneurons).
Then(_x => firsthiddenlayerneurons).
Then(_xxx => evolutions).
Then(_xxxx => learningrate));
selforganisingmap = Parser.Literal("selforganisingmap").CreateAlgorithmus(netMLObjectBuilder).
Then(_ => inputneurons.And(outputneurons));
}
private Parse <string> CreateKmetroids(NetMLObjectBuilder netMLObjectBuilder)
{
var createKmetroids = Parser.Literal("kmetroids").CreateAlgorithmus(netMLObjectBuilder);
return(createKmetroids);
}
private Parse <string> CreateLinearregression(NetMLObjectBuilder netMLObjectBuilder)
{
var linearregression = Parser.Literal("linearregression").CreateAlgorithmus(netMLObjectBuilder);
return(linearregression);
}
private Parse <string> CreateApriori(NetMLObjectBuilder netMLObjectBuilder)
{
var apriori = Parser.Literal("apriori").CreateAlgorithmus(netMLObjectBuilder);
return(apriori);
}
private Parse <string> ParserObject(NetMLObjectBuilder netMLObjectBuilder)
{
var createKNN = CreateKNN(netMLObjectBuilder);
var createLogisticregression = CreateLogisticregression(netMLObjectBuilder);
var createdecisiontree = CreateDecisiontree(netMLObjectBuilder);
var createrandomforest = CreateRandomforest(netMLObjectBuilder);
var createnaivebayers = CreateNaivebayers(netMLObjectBuilder);
Parse <string> backpropagation;
Parse <string> radialbasisfunction;
Parse <string> selforganisingmap;
NeuronalNetworks(netMLObjectBuilder, out backpropagation, out radialbasisfunction, out selforganisingmap);
Parse <string> supportvectormachine;
Parse <string> dualperceptron;
CreateSupportVectorMachine(netMLObjectBuilder, out supportvectormachine, out dualperceptron);
var KMeans = CreateKMeans(netMLObjectBuilder);
var linearregression = CreateLinearregression(netMLObjectBuilder);
var apriori = CreateApriori(netMLObjectBuilder);
var createClassification = Parser.Literal("classification").
CreateClassificationAlgorithmusObject(netMLObjectBuilder).
Then(_ => createKNN.
Or(createLogisticregression).
Or(createdecisiontree).
Or(createrandomforest).
Or(createnaivebayers).
Or(supportvectormachine).
Or(backpropagation).
Or(radialbasisfunction).
Or(selforganisingmap));
//var createClustering = Parser.Literal("clustering").CreateClassificationAlgorithmusObject(netMLObjectBuilder)
// .Then(_ => KMeans);
var createClustering = CreateClustering(netMLObjectBuilder);
var createRegression = Parser.Literal("regression").CreateClassificationAlgorithmusObject(netMLObjectBuilder)
.Then(_ => linearregression);
var createAssociation = Parser.Literal("association").CreateClassificationAlgorithmusObject(netMLObjectBuilder)
.Then(_ => apriori);
var createLiteral = Parser.StringLiteral("create").CreateNetMLObject(netMLObjectBuilder).
Then(_ => createClassification.Or(createClustering).Or(createRegression).Or(createAssociation));
var doubleParserValue = Parser.DecimalString().CreateValue(netMLObjectBuilder);
var integerParserValue = Parser.Integer().CreateValue(netMLObjectBuilder);
var variable = Parser.StringValue().CreateVariable(netMLObjectBuilder);
return(createLiteral);
}