/// <summary>
/// 行列 X に主成分分析を適用する．
/// </summary>
/// <param name="X">
/// <para>[n, d]の行列（書き換えられることはない）</para>
/// <para>データ数：n，次元数：dとしたとき，行列 X の形式は n×d でなければならない．</para>
/// </param>
/// <param name="varType">分散の種類</param>
public PrincipalComponentAnalysis(Matrix X, VarianceType varType)
{
Matrix _X = new Matrix(X);
// 各変数の平均値を0にする
Vector colAvg = _X.ColumnAverages;
_X.Columns.ForEach(delegate(int i, IVector v)
{
v.ForEach(delegate(ref double val)
{
val -= colAvg[i];
});
});
// 分散・共分散行列
Matrix S = Matrix.Transpose(_X) * _X;
if (varType == VarianceType.DivN)
{
S /= _X.RowSize;
}
else
{
S /= (_X.RowSize - 1);
}
// 固有値分解
EigenvalueDecomposition evd = new EigenvalueDecomposition(S);
evd.Sort(EigenvalueDecomposition.SortOrder.Descending);
this.eigenvalues = evd.RealEigenvalues;
this.eigenvectors = evd.RealEigenvectors;
}
/// <summary>
/// Creates the structures parameter row.
/// </summary>
/// <param name="readText">The read text.</param>
/// <returns>The created structures parameter row.</returns>
/// <exception cref="LineParseException">Thrown when either:
/// <list type="bullet">
/// <item><paramref name="readText"/> does not contain the separator character.</item>
/// <item>Location id field is empty or consists only of white-space characters.</item>
/// <item>Parameter id field is empty or consists only of white-space characters.</item>
/// <item>Numeric value field is not a number or too large/small to be represented as <see cref="double"/>.</item>
/// <item>Variance value field is not a number or too large/small to be represented as <see cref="double"/>.</item>
/// <item>Boolean field is not a valid value.</item>
/// </list></exception>
private StructuresParameterRow CreateStructuresParameterRow(string readText)
{
string[] tokenizedText = TokenizeString(readText);
if (tokenizedText.Length != headerLength)
{
string message = string.Format(Resources.StructuresCharacteristicsCsvReader_CreateStructuresParameterRow_Line_should_have_NumberOfExpectedElements_0_but_has_ActualNumberOfElements_1_,
headerLength, tokenizedText.Length);
throw CreateLineParseException(lineNumber, message);
}
string locationId = ParseLocationId(tokenizedText);
string parameterId = ParseParameterId(tokenizedText);
string alphanumericValue = ParseAlphanumericValue(tokenizedText);
double numericValue = ParseNumericValue(tokenizedText);
double varianceValue = ParseVarianceValue(tokenizedText);
VarianceType varianceType = ParseVarianceType(tokenizedText);
return(new StructuresParameterRow
{
LocationId = locationId,
ParameterId = parameterId,
AlphanumericValue = alphanumericValue,
NumericalValue = numericValue,
VarianceValue = varianceValue,
VarianceType = varianceType,
LineNumber = lineNumber
});
}
/// <summary>
/// �s�� X �Ɏ听�����͂�K�p����D
/// </summary>
/// <param name="X">
/// <para>[n, d]�̍s��i�����������邱�Ƃ͂Ȃ��j</para>
/// <para>�f�[�^���Fn�C�������Fd�Ƃ����Ƃ��C�s�� X �̌`���� n�~d �łȂ���Ȃ�Ȃ��D</para>
/// </param>
/// <param name="varType">���U�̎��</param>
public PrincipalComponentAnalysis(Matrix X, VarianceType varType)
{
Matrix _X = new Matrix(X);
// �e�ϐ��̕��ϒl��0�ɂ���
Vector colAvg = _X.ColumnAverages;
_X.Columns.ForEach(delegate(int i, IVector v)
{
v.ForEach(delegate(ref double val)
{
val -= colAvg[i];
});
});
// ���U�E�����U�s��
Matrix S = Matrix.Transpose(_X) * _X;
if (varType == VarianceType.DivN)
{
S /= _X.RowSize;
}
else
{
S /= (_X.RowSize - 1);
}
// �ŗL�l����
EigenvalueDecomposition evd = new EigenvalueDecomposition(S);
evd.Sort(EigenvalueDecomposition.SortOrder.Descending);
this.eigenvalues = evd.RealEigenvalues;
this.eigenvectors = evd.RealEigenvectors;
}
public static double StandardDeviation(this double[] input, VarianceType type)
{
if (!input.Any())
{
throw new InvalidOperationException(Consts.EMPTY_ARRAY);
}
return(Math.Sqrt(input.Variance(type)));
}
private static SyntaxKind GetVarianceToken(VarianceType variance)
{
switch (variance)
{
case VarianceType.Contravariant:
return(SyntaxKind.InKeyword);
case VarianceType.Covariant:
return(SyntaxKind.OutKeyword);
default:
return(SyntaxKind.None);
}
}
public static double Variance(this double[] input, VarianceType type)
{
if (!input.Any())
{
throw new InvalidOperationException(Consts.EMPTY_ARRAY);
}
else if (input.Count() == 1 && type == VarianceType.Sample)
{
throw new InvalidOperationException(Consts.INVALID_SAMPLE_FOR_VARIANCE);
}
var mean = input.Mean();
int divisor = type == VarianceType.Population ? input.Count() : input.Count() - 1;
return(input.Sum(q => Math.Pow(q - mean, 2)) / divisor);
}
private static T _Standardize_T <T>(T vector, VarianceType varType) where T : IVector
{
VectorChecker.ZeroSize(vector);
double avg = vector.Average;
double std = Math.Sqrt(varType == VarianceType.DivN ? vector.Scatter / vector.Size : vector.Variance);
if (clapack.CalculationChecker.IsLessThanLimit(std))
{
throw new Exception.ValueIsLessThanLimitException(
"The standard variation is less than \"Calculation Limit\".\n"
+ "Values = " + vector.ToString());
}
for (int i = 0; i < vector.Size; ++i)
{
vector[i] = (vector[i] - avg) / std;
}
return(vector);
}
/// <summary>
/// 指定された行列を標準化する．
/// </summary>
/// <param name="X">標準化する行列（直接変更されない）</param>
/// <param name="target">行ごとの標準化なのか，列ごとの標準化なのかを指定する</param>
/// <param name="varType">標準化に使用される分散のタイプ</param>
/// <returns>Xのコピーを標準化した行列</returns>
///
/// <exception cref="Exception.ValueIsLessThanLimitException">
/// 標準偏差値が設定されている精度の下限値未満の場合にthrowされる．
/// </exception>
public static Matrix Standardize(Matrix X, Target target, VarianceType varType)
{
Matrix ret = new Matrix(X);
if (target == Target.EachRow)
{
for (int r = 0; r < ret.RowSize; ++r)
{
ret.Rows[r] = Standardize(ret.Rows[r], varType);
}
}
else
{
for (int c = 0; c < ret.ColumnSize; ++c)
{
ret.Columns[c] = Standardize(ret.Columns[c], varType);
}
}
return(ret);
}
/// <summary>
/// 分散
/// </summary>
/// <param name="data"></param>
/// <param name="type"></param>
/// <returns></returns>
public static double Variance(IEnumerable<double> data, VarianceType type = VarianceType.Unbiased)
{
CheckArgument(data);
var mean = Mean(data);
double n = data.Count();
if (type == VarianceType.Unbiased && n == 1)
{
throw new InvalidOperationException("標本数1の場合不偏分散は計算できません");
}
var temp = data.Select(x => (mean - x) * (mean - x)).Sum();
if (type == VarianceType.Sample)
{
return temp / n;
}
else
{
return temp / (n - 1.0);
}
}
private static List <string> ValidateStochasticVariableParameters(StructuresParameterRow row, bool meanMustBeGreaterThanZero, bool variationAsStandardDeviation)
{
List <string> messages = ValidateStochasticVariableMeanParameter(row, meanMustBeGreaterThanZero);
VarianceType type = row.VarianceType;
if (type != VarianceType.StandardDeviation && type != VarianceType.CoefficientOfVariation)
{
messages.Add(string.Format(Resources.StructuresParameterRowsValidator_ParameterId_0_Line_1_ColumnName_2_invalid_variancetype_value,
row.ParameterId, row.LineNumber, StructureFilesKeywords.VariationTypeColumnName.FirstToUpper()));
}
messages.AddRange(ValidatePositiveDoubleParameter(row, StructureFilesKeywords.VariationValueColumnName));
double mean = row.NumericalValue;
double absoluteMean = Math.Abs(mean);
if (variationAsStandardDeviation)
{
if (row.VarianceType == VarianceType.CoefficientOfVariation && absoluteMean < valueTooCloseToZero)
{
messages.Add(string.Format(Resources.StructuresParameterRowsValidator_ParameterId_0_Line_1_ColumnName_2_mean_too_small_for_reliable_variation_value_conversion,
row.ParameterId, row.LineNumber, StructureFilesKeywords.NumericalValueColumnName.FirstToUpper()));
}
}
else
{
if (row.VarianceType == VarianceType.StandardDeviation && absoluteMean < valueTooCloseToZero)
{
messages.Add(string.Format(Resources.StructuresParameterRowsValidator_ParameterId_0_Line_1_ColumnName_2_mean_too_small_for_reliable_variation_value_conversion,
row.ParameterId, row.LineNumber, StructureFilesKeywords.NumericalValueColumnName.FirstToUpper()));
}
}
return(messages);
}
/// <summary>
/// 共分散
/// </summary>
/// <param name="data1"></param>
/// <param name="data2"></param>
/// <returns></returns>
public static double Covariance(IEnumerable<double> data1, IEnumerable<double> data2, VarianceType type = VarianceType.Unbiased)
{
CheckArgument(data1, "data1");
CheckArgument(data2, "data2");
if (data1.Count() != data2.Count())
{
throw new InvalidOperationException("data1とdata2の要素数が異なるため共分散を計算できません");
}
var n = data1.Count();
var mean1 = Mean(data1);
var mean2 = Mean(data2);
var sum = data1.Select((x, i) => (x - mean1) * (data2.ElementAt(i) - mean2)).Sum();
if (type == VarianceType.Unbiased)
{
return sum / (n - 1);
}
else
{
return sum / n;
}
}
/// <summary>
/// 共分散
/// </summary>
/// <param name="data"></param>
/// <param name="type"></param>
/// <returns></returns>
public static double Covariance(IEnumerable<VectorD> data, int index1, int index2, VarianceType type = VarianceType.Unbiased)
{
CheckArgument(data);
return Covariance(data.Select(x => x[index1]), data.Select(x => x[index2]), type);
}
/// <summary>
/// 共分散
/// </summary>
/// <param name="data1"></param>
/// <param name="data2"></param>
/// <returns></returns>
public static double Covariance(IEnumerable<double> data1, IEnumerable<double> data2, VarianceType type = VarianceType.Unbiased)
{
CheckArgument(data1, "data1");
CheckArgument(data2, "data2");
CheckArgumentLength(data1, data2);
var n = data1.Count();
var mean1 = Mean(data1);
var mean2 = Mean(data2);
var sum = data1.Select((x, i) => (x - mean1) * (data2.ElementAt(i) - mean2)).Sum();
if (type == VarianceType.Unbiased)
{
return sum / (n - 1);
}
else
{
return sum / n;
}
}
/// <summary>
/// 標準偏差
/// </summary>
/// <param name="data"></param>
/// <param name="type"></param>
/// <returns></returns>
public static double StdDev(IEnumerable<double> data, VarianceType type = VarianceType.Unbiased)
{
CheckArgument(data);
var variance = Variance(data, type);
return Math.Sqrt(variance);
}
public void ReadLine_ValidFile_ReturnDataFromLine(int elementIndex, string expectedLocationId, string paramterId,
double expectedNumericValue, double expectedVarianceValue, VarianceType expectedType)
{
// Setup
string filePath = TestHelper.GetTestDataPath(TestDataPath.Riskeer.Common.IO,
Path.Combine("Structures", "StructuresCharacteristicsCsvFiles", "ValidFile_2Locations_AllHeightStructureParameters.csv"));
using (var reader = new StructuresCharacteristicsCsvReader(filePath))
{
// Call
StructuresParameterRow parameter = null;
for (var i = 0; i < elementIndex; i++)
{
parameter = reader.ReadLine();
}
// Assert
Assert.IsNotNull(parameter);
Assert.AreEqual(expectedLocationId, parameter.LocationId);
Assert.AreEqual(paramterId, parameter.ParameterId);
Assert.AreEqual(expectedNumericValue, parameter.NumericalValue);
Assert.AreEqual(expectedVarianceValue, parameter.VarianceValue);
Assert.AreEqual(expectedType, parameter.VarianceType);
Assert.AreEqual(elementIndex + 1, parameter.LineNumber);
}
}
public void SimpleProperties_SetNewValues_GetNewlySetValues(string locationId, string id, string alphanumeric, double numerical, double variance, VarianceType type, int lineNumber)
{
// Setup
var parameter = new StructuresParameterRow();
// Call
parameter.LocationId = locationId;
parameter.ParameterId = id;
parameter.AlphanumericValue = alphanumeric;
parameter.NumericalValue = numerical;
parameter.VarianceValue = variance;
parameter.VarianceType = type;
parameter.LineNumber = lineNumber;
// Assert
Assert.AreEqual(locationId, parameter.LocationId);
Assert.AreEqual(id, parameter.ParameterId);
Assert.AreEqual(alphanumeric, parameter.AlphanumericValue);
Assert.AreEqual(numerical, parameter.NumericalValue);
Assert.AreEqual(variance, parameter.VarianceValue);
Assert.AreEqual(type, parameter.VarianceType);
Assert.AreEqual(lineNumber, parameter.LineNumber);
}
/// <summary>
/// 標準化された<see cref="IVector"/>インタフェース実装オブジェクトを作成する．
/// このメソッドは，インタフェースで受け取り，インタフェースで返す．
/// </summary>
/// <param name="vector">
/// 標準化される<see cref="IVector"/>インタフェース実装オブジェクト（実際は，この引数のコピーが標準化される）
/// </param>
/// <param name="varType">標準化に使用される分散の種類</param>
/// <returns>標準化された<see cref="IVector"/>インタフェース実装オブジェクト</returns>
///
/// <exception cref="Exception.ValueIsLessThanLimitException">
/// 標準偏差値が設定されている精度の下限値未満の場合にthrowされる．
/// </exception>
public static IVector Standardize(IVector vector, VarianceType varType)
{
return(_Standardize_T(new Vector(vector), varType));
}
public int GetVariance(VarianceType varianceType)
{
switch (varianceType)
{
case VarianceType.All:
//return Convert.ToInt16(_Variance.All);
return(0);
case VarianceType.Q1:
return(Convert.ToInt16(_Variance.Q1));
case VarianceType.Q2:
return(Convert.ToInt16(_Variance.Q2));
case VarianceType.Q3:
return(Convert.ToInt16(_Variance.Q3));
case VarianceType.Q4:
return(Convert.ToInt16(_Variance.Q4));
case VarianceType.QAverage:
return(Convert.ToInt16(_Variance.QAverage));
case VarianceType.S1:
return(Convert.ToInt16(_Variance.S1));
case VarianceType.S2:
return(Convert.ToInt16(_Variance.S2));
case VarianceType.S3:
return(Convert.ToInt16(_Variance.S3));
case VarianceType.S4:
return(Convert.ToInt16(_Variance.S4));
case VarianceType.S5:
return(Convert.ToInt16(_Variance.S5));
case VarianceType.S6:
return(Convert.ToInt16(_Variance.S6));
case VarianceType.S7:
return(Convert.ToInt16(_Variance.S7));
case VarianceType.S8:
return(Convert.ToInt16(_Variance.S8));
case VarianceType.S9:
return(Convert.ToInt16(_Variance.S9));
case VarianceType.Savg:
return(Convert.ToInt16(_Variance.Savg));
case VarianceType.L1:
return(Convert.ToInt16(_Variance.L1));
case VarianceType.L2:
return(Convert.ToInt16(_Variance.L2));
case VarianceType.L3:
return(Convert.ToInt16(_Variance.L3));
case VarianceType.L4:
return(Convert.ToInt16(_Variance.L4));
case VarianceType.L5:
return(Convert.ToInt16(_Variance.L5));
case VarianceType.L6:
return(Convert.ToInt16(_Variance.L6));
case VarianceType.L7:
return(Convert.ToInt16(_Variance.L7));
case VarianceType.L8:
return(Convert.ToInt16(_Variance.L8));
case VarianceType.L9:
return(Convert.ToInt16(_Variance.L9));
case VarianceType.L10:
return(Convert.ToInt16(_Variance.L10));
case VarianceType.L11:
return(Convert.ToInt16(_Variance.L11));
case VarianceType.L12:
return(Convert.ToInt16(_Variance.L12));
case VarianceType.L13:
return(Convert.ToInt16(_Variance.L13));
case VarianceType.L14:
return(Convert.ToInt16(_Variance.L14));
case VarianceType.L15:
return(Convert.ToInt16(_Variance.L15));
case VarianceType.L16:
return(Convert.ToInt16(_Variance.L16));
case VarianceType.Lavg:
return(Convert.ToInt16(_Variance.Lavg));
default:
return(0);
}
}
/// <summary>
/// 標準化された<see cref="ColumnVector"/>を作成する．
/// </summary>
/// <param name="vector">標準化される<see cref="ColumnVector"/>（実際は，この引数のコピーが標準化される）</param>
/// <param name="varType">標準化に使用する分散の種類</param>
/// <returns>標準化された<see cref="ColumnVector"/></returns>
///
/// <exception cref="Exception.ValueIsLessThanLimitException">
/// 標準偏差値が設定されている精度の下限値未満の場合にthrowされる．
/// </exception>
public static ColumnVector Standardize(ColumnVector vector, VarianceType varType)
{
return(_Standardize_T(new ColumnVector(vector), varType));
}