Class Vec

Utility functions and operations for treating arrays as mathematical vectors.

This class considers doubles, methods that require comparisons cannot be easily ported to generic "Vec"/>

Inheritance

object

Vec

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Namespace: TimeSeriesAnalysis

Assembly: TimeSeriesAnalysis.dll

Syntax

public class Vec

Constructors

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Vec(double, double)

Constructor

Declaration

public Vec(double nanValue = -9999, double valuteToReturnElementIsNaN = NaN)

Parameters

Type	Name	Description
double	nanValue	inputs values matching this value are treated as "NaN" and are excluded from all calculations
double	valuteToReturnElementIsNaN	value to return in elementwise calculations to indiate NaN output

Methods

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Abs(double[])

Returns an array where each value is the absolute value of array1.

Declaration

public double[] Abs(double[] array1)

Parameters

Type	Name	Description
double[]	array1

Returns

Type	Description
double[]

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Add(double[], double)

Elementwise addition of val2 to array1.

Declaration

public double[] Add(double[] array1, double val2)

Parameters

Type	Name	Description
double[]	array1
double	val2

Returns

Type	Description
double[]

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Add(double[], double[])

Returns an array which is the elementwise addition of array1 and array2.

Declaration

public double[] Add(double[] array1, double[] array2)

Parameters

Type	Name	Description
double[]	array1
double[]	array2

Returns

Type	Description
double[]

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ContainsBadData(double[])

Returns true if array contains a "-9999" or NaN indicating missing data.

Declaration

public bool ContainsBadData(double[] x)

Parameters

Type	Name	Description
double[]	x

Returns

Type	Description
bool

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Cov(double[], double[], bool)

Returns the co-variance of two arrays (interpreted as "vectors").

Declaration

public double Cov(double[] array1, double[] array2, bool doNormalize = false)

Parameters

Type	Name	Description
double[]	array1
double[]	array2
bool	doNormalize

Returns

Type	Description
double

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Deserialize(string)

De-serializes a single vector/array (written by serialize).

Declaration

public static double[] Deserialize(string fileName)

Parameters

Type	Name	Description
string	fileName

Returns

Type	Description
double[]

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Diff(double[], List<int>)

Return an array of the differences between the neighboring items in array but ignores indices in the array that are in "indicesToIgnore".

Declaration

public double[] Diff(double[] vec, List<int> indicesToIgnore = null)

Parameters

Type	Name	Description
double[]	vec
List<int>	indicesToIgnore

Returns

Type	Description
double[]

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Div(double[], double)

Divides a vector by a scalar value.

Declaration

public double[] Div(double[] vector, double scalar)

Parameters

Type	Name	Description
double[]	vector
double	scalar

Returns

Type	Description
double[]	a vector of values representing the array divided by a scalar. In case of NaN inputs or divide-by-zero, NaN elements are returned.

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Div(double[], double[])

Divides two vectors of equal length.

Declaration

public double[] Div(double[] vector1, double[] vector2)

Parameters

Type	Name	Description
double[]	vector1
double[]	vector2

Returns

Type	Description
double[]	a vector of values representing the array divided by a scalar. In case of NaN inputs or divide-by-zero, NaN elements are returned.

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Equal(double[], double[])

Returns true if the two vectors are equal

Declaration

public static bool Equal(double[] arr1, double[] arr2)

Parameters

Type	Name	Description
double[]	arr1
double[]	arr2

Returns

Type	Description
bool

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FindValues(double[], double, VectorFindValueType, List<int>)

Return the indices of elements in the array that have a certain relation to value given type (bigger, smaller, equal, etc.). Also capable of finding NaN values.

Declaration

public List<int> FindValues(double[] vec, double value, VectorFindValueType type, List<int> indicesToIgnore = null)

Parameters

Type	Name	Description
double[]	vec
double	value
VectorFindValueType	type
List<int>	indicesToIgnore	if given, these indices are ignored when doing the operation

Returns

Type	Description
List<int>

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GetGradient(double[], DateTime[], int, int[])

Gets the gradient of a time-series.

Works by running a regression with time as the "X" variable

Declaration

public static RegressionResults GetGradient(double[] values, DateTime[] dates, int sampleTime_sec = 1, int[] indicesToIgnore = null)

Parameters

Type	Name	Description
double[]	values	values for which the gradient is sought
DateTime[]	dates	dates corresponding to the values
int	sampleTime_sec	in what unit of time (given in seconds)the gradient shall be presented
int[]	indicesToIgnore	optional array of indices that are to be ignored during regression

Returns

Type	Description
RegressionResults	the gradient will be the "Gain" of the returned object (in units per second by default).

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GetValues(double[], List<int>)

Returns all the values of vec, except for those corresponding with indices in "indicesToIngore".

Declaration

public double[] GetValues(double[] vec, List<int> indicesToIgnore)

Parameters

Type	Name	Description
double[]	vec
List<int>	indicesToIgnore

Returns

Type	Description
double[]

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IsAllNaN(double[])

Returns true if all elements in the array are "-9999" or Double.NaN, or is null.

Declaration

public bool IsAllNaN(double[] array)

Parameters

Type	Name	Description
double[]	array

Returns

Type	Description
bool

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IsAllValue(double[], double)

Returns true if all elements in the array are the specific value.

Declaration

public static bool IsAllValue(double[] array, double value = 0)

Parameters

Type	Name	Description
double[]	array
double	value

Returns

Type	Description
bool

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Max(double[])

Returns the maximum value of the array.

Declaration

public double Max(double[] array)

Parameters

Type	Name	Description
double[]	array

Returns

Type	Description
double

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Max(double[], List<int>)

Returns the maximum value of the array, ignoring the given indices.

Declaration

public double Max(double[] array, List<int> indicesToIgnore)

Parameters

Type	Name	Description
double[]	array
List<int>	indicesToIgnore

Returns

Type	Description
double

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Max(double[], double)

Returns the element-wise maximum of the array element and value.

Declaration

public double[] Max(double[] array, double value)

Parameters

Type	Name	Description
double[]	array
double	value

Returns

Type	Description
double[]

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Max(double[], double[])

Returns the maximum value of two arrays as a new array.

Declaration

public double[] Max(double[] array1, double[] array2)

Parameters

Type	Name	Description
double[]	array1
double[]	array2

Returns

Type	Description
double[]

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Max(double[], out int)

Returns the maximum value of the array and the index of the maximum value.

Declaration

public double Max(double[] array, out int ind)

Parameters

Type	Name	Description
double[]	array
int	ind

Returns

Type	Description
double

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Mean(double[])

Returns the mean value of array1.

Declaration

public double? Mean(double[] array1)

Parameters

Type	Name	Description
double[]	array1

Returns

Type	Description
double?

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Mean(double[], List<int>)

Returns the mean value of array1. while ignoring given indices

Declaration

public double? Mean(double[] array1, List<int> indToIgnore = null)

Parameters

Type	Name	Description
double[]	array1
List<int>	indToIgnore

Returns

Type	Description
double?

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Min(double[])

Returns the minimum value of the array.

Declaration

public double Min(double[] array)

Parameters

Type	Name	Description
double[]	array

Returns

Type	Description
double

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Min(double[], List<int>)

Returns the minimum value of the array, ignoring certain indices.

Declaration

public double Min(double[] array, List<int> indicesToIgnore)

Parameters

Type	Name	Description
double[]	array
List<int>	indicesToIgnore

Returns

Type	Description
double

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Min(double[], double)

Returns the element-wise minimum of the array elements and value.

Declaration

public double[] Min(double[] array, double value)

Parameters

Type	Name	Description
double[]	array
double	value

Returns

Type	Description
double[]

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Min(double[], double[])

Returns the minimum value of the two arrays as new array.

Declaration

public static double[] Min(double[] array1, double[] array2)

Parameters

Type	Name	Description
double[]	array1
double[]	array2

Returns

Type	Description
double[]

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Min(double[], out int)

Returns the minimum value of the array and the index of the maximum value.

Declaration

public double Min(double[] array, out int ind)

Parameters

Type	Name	Description
double[]	array
int	ind

Returns

Type	Description
double

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Multiply(double[], double)

Elementwise multiplication of val2 to array1.

Declaration

public double[] Multiply(double[] array1, double val2)

Parameters

Type	Name	Description
double[]	array1
double	val2

Returns

Type	Description
double[]

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Multiply(double[], double[])

Elementwise multiplication of array1 and array2, assuming they are same size.

Declaration

public double[] Multiply(double[] array1, double[] array2)

Parameters

Type	Name	Description
double[]	array1
double[]	array2

Returns

Type	Description
double[]

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NonCausalSmooth(double[], int)

Smooths the array without phase-shifting by using both past and future values (i.e. so called non-causal smoothing).

Declaration

public double[] NonCausalSmooth(double[] array1, int kernel = 1)

Parameters

Type	Name	Description
double[]	array1
int	kernel

Returns

Type	Description
double[]

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Pow(double[], double)

Calculates the power of the array.

Declaration

public double[] Pow(double[] array, double factor)

Parameters

Type	Name	Description
double[]	array
double	factor

Returns

Type	Description
double[]

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RSquared(double[], double[], List<int>, int)

R-squared. R-squared (R2) is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. Whereas correlation explains the strength of the relationship between an independent and dependent variable, R-squared explains to what extent the variance of one variable explains the variance of the second variable. So, if the R2 of a model is 0.50, then approximately half of the observed variation can be explained by the model's inputs.

Declaration

public double RSquared(double[] vector1, double[] vector2, List<int> indToIgnoreExt = null, int ymodOffset = -1)

Parameters

Type	Name	Description
double[]	vector1	first vector
double[]	vector2	second vector
List<int>	indToIgnoreExt	optionally: indices to be ignored (for instance bad values)
int	ymodOffset	set the offset beteen vector1 and vector2 (for difference equations, ymod is offset by -1 from ymeas). -1 is default.

Returns

Type	Description
double	R2 squared, a value between `-1` and `1`. If an error occured, `Double.PositiveInfinity` is returned.

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Rand(int, double, double, int?)

Create a vector of random numbers.

Declaration

public static double[] Rand(int N, double minValue = 0, double maxValue = 1, int? seed = null)

Parameters

Type	Name	Description
int	N	the number of samples of the returned array
double	minValue	lower end of random number range
double	maxValue	higher end of random number range
int?	seed	optionally, give in a seed number, this makes random sequence repeatable

Returns

Type	Description
double[]	an array of size N of random numbers between minValue and maxValue.

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Range(double[])

Returns the range of an array; the difference between minimum and maximum element values.

Declaration

public double Range(double[] array)

Parameters

Type	Name	Description
double[]	array

Returns

Type	Description
double

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Regress(double[], double[,], int[])

Robust linear regression.

Declaration

public RegressionResults Regress(double[] Y, double[,] X, int[] yIndToIgnore = null)

Parameters

Type	Name	Description
double[]	Y	vector of response variable values (to be modelled)
double[,]	X	2D matrix of manipulated values/independent values/regressors used to explain Y
int[]	yIndToIgnore	(optional) a list of the indices of values in Y to ignore in regression. By default it is `null`

Returns

Type	Description
RegressionResults	an object of the `RegressionResult` class with the parameters, as well as some statistics on the fit and uncertainty thereof.

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RegressRegularized(double[], double[][], int[], List<int>)

Robust linear regression, regularized. To avoid parameters taking on extremely high values in the case of a little excitation in the inputs, two mitigating actions are implemented by the solver to be "robust":

a "robust" Singular Value Decomposition (SVD) -based solver is used.
a regularization term is added to the objective function that will bring parameters to zero if (Y,X) does not contain any information to force the parameter away from zero.

Declaration

public RegressionResults RegressRegularized(double[] Y, double[][] X, int[] yIndToIgnore = null, List<int> XindicesToRegularize = null)

Parameters

Type	Name	Description
double[]	Y	vector of output variable values to be modelled
double[][]	X	jagged 2D matrix of manipulated values/independent values/regressors used to explain Y
int[]	yIndToIgnore	(optional) a list of the indices of values in Y to ignore in regression. By default it is `null`
List<int>	XindicesToRegularize	(optional) only the indices in this list are to be regularized to zero

Returns

Type	Description
RegressionResults	an object of the `RegressionResult` class with the parameters, as well as some statistics on the fit and uncertainty thereof.

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RegressUnRegularized(double[], double[][], int[])

Regression that does not attempt to regualarize inputs toward zero.

Declaration

public RegressionResults RegressUnRegularized(double[] Y, double[][] X, int[] yIndToIgnore = null)

Parameters

Type	Name	Description
double[]	Y
double[][]	X
int[]	yIndToIgnore

Returns

Type	Description
RegressionResults

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ReplaceIndWithValue(double[], List<int>, double)

Replace certain values in an array with a new value.

Declaration

public static double[] ReplaceIndWithValue(double[] array, List<int> indList, double valueToReplaceWith)

Parameters

Type	Name	Description
double[]	array	the array to be replaced
List<int>	indList	list of all the indices of all data points in the array to be replaced
double	valueToReplaceWith	the new value to use in place of old values

Returns

Type	Description
double[]	a copy of the original array with the values replaced as specified.

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ReplaceValuesAbove(double[], double, double)

Replace values below a certain threshold in an array with a new value.

Declaration

public static double[] ReplaceValuesAbove(double[] array, double threshold, double valueToReplaceWith)

Parameters

Type	Name	Description
double[]	array
double	threshold
double	valueToReplaceWith

Returns

Type	Description
double[]

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ReplaceValuesAboveOrBelow(double[], double, double, double)

Replace values above a higher threshold or below a lower threshold with a new value.

Declaration

public static double[] ReplaceValuesAboveOrBelow(double[] array, double lowerThreshold, double higherThreshold, double valueToReplaceWith)

Parameters

Type	Name	Description
double[]	array
double	lowerThreshold
double	higherThreshold
double	valueToReplaceWith

Returns

Type	Description
double[]

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ReplaceValuesBelow(double[], double, double)

Replace all values above a certain threshold in array with a new value.

Declaration

public static double[] ReplaceValuesBelow(double[] array, double threshold, double valueToReplaceWith)

Parameters

Type	Name	Description
double[]	array
double	threshold
double	valueToReplaceWith

Returns

Type	Description
double[]

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SelfSumOfAbsErr(double[])

Sum of the absolute errors of the vector compared to itself.

Declaration

public double SelfSumOfAbsErr(double[] vec)

Parameters

Type	Name	Description
double[]	vec

Returns

Type	Description
double

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SelfSumOfSquareErr(double[])

Sum of the square errors of the vector compared to itself

Declaration

public double SelfSumOfSquareErr(double[] vec)

Parameters

Type	Name	Description
double[]	vec

Returns

Type	Description
double

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Serialize(double[], string)

Serializes a single vector/array to a file for persistent storage to a human-readable text format. Vector data can then be retreived by companion method Deserialize.

Declaration

public static bool Serialize(double[] vector, string fileName)

Parameters

Type	Name	Description
double[]	vector	vector to be written to a file
string	fileName	the file name (or path) of the file to which the vector is to serialized to

Returns

Type	Description
bool

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Subtract(double[], double)

Elementwise subtraction of val2 from array1.

Declaration

public double[] Subtract(double[] array1, double val2)

Parameters

Type	Name	Description
double[]	array1
double	val2

Returns

Type	Description
double[]

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Subtract(double[], double[])

Elementwise subtraction of array1 and array2, assuming they are same size.

Declaration

public double[] Subtract(double[] array1, double[] array2)

Parameters

Type	Name	Description
double[]	array1
double[]	array2

Returns

Type	Description
double[]

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Sum(double[])

Returns the elementwise sum of array1.

Declaration

public double? Sum(double[] array1)

Parameters

Type	Name	Description
double[]	array1

Returns

Type	Description
double?

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SumOfAbsErr(double[], double[], int)

The sum of absolute errors (|a1-a2|) between array1 and array2.

Declaration

public double SumOfAbsErr(double[] array1, double[] array2, int indexOffset = -1)

Parameters

Type	Name	Description
double[]	array1
double[]	array2
int	indexOffset

Returns

Type	Description
double

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SumOfSquareErr(double[], double, bool, List<int>)

Sum of the square errors of the vector compared to a constant. by default the return value is normalized by dividing by the number of elements; this normalization can be turned off.

Declaration

public static double SumOfSquareErr(double[] vec, double constant, bool doNormalization = true, List<int> indToIgnore = null)

Parameters

Type	Name	Description
double[]	vec
double	constant
bool	doNormalization
List<int>	indToIgnore

Returns

Type	Description
double

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SumOfSquareErr(double[], double[], int, bool, List<int>)

The sum of square errors (a1-a2)^2 between array1 and array2.

Declaration

public double SumOfSquareErr(double[] array1, double[] array2, int ymodOffset = -1, bool divByN = true, List<int> indToIgnore = null)

Parameters

Type	Name	Description
double[]	array1
double[]	array2
int	ymodOffset
bool	divByN	if true, the result is normalized by the number of good values
List<int>	indToIgnore	optionally a list of indices of `array1` to ignore

Returns

Type	Description
double

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ToString(double[], int, string)

Create a compact string of vector with a certain number of significant digits and a chosen divider.

Declaration

public static string ToString(double[] array, int nSignificantDigits, string dividerStr = ";")

Parameters

Type	Name	Description
double[]	array
int	nSignificantDigits
string	dividerStr

Returns

Type	Description
string

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Var(double[], bool)

Returns the variance of the array (always a positive number).

Declaration

public double Var(double[] array1, bool doNormalize = false)

Parameters

Type	Name	Description
double[]	array1
bool	doNormalize

Returns

Type	Description
double