Méthode | Description | |
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EmpiricalCDF ( this |
Estimates the pointwise empirical cumulative distribution function (CDF) at x from the provided samples.
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EmpiricalInvCDF ( this |
Estimates the pointwise empirical inverse CDF at tau from the provided samples.
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Entropy ( this |
Calculates the pointwise entropy of the vectors in bits. Returns NaN if any of the values in the stream are NaN.
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InterquartileRange ( this |
Estimates the pointwise inter-quartile range from the vectors. Approximately median-unbiased regardless of the sample distribution (R8).
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Kurtosis ( this |
Estimates the pointwise unbiased population kurtosis from the provided samples. Uses a normalizer (Bessel's correction; type 2). Returns NaN if data has less than four entries or if any entry is NaN.
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LowerQuartile ( this |
Estimates the pointwise first quartile value from the vectors. Approximately median-unbiased regardless of the sample distribution (R8).
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Maximum ( this |
Returns the pointwise smallest value from the vectors. Returns NaN if data is empty or any entry is NaN.
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Maximum ( this |
Returns the pointwise smallest value from the vectors. Returns NaN if data is empty or any entry is NaN.
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Mean ( this |
Estimates the pointwise arithmetic sample mean from the vectors. Returns NaN if data is empty or any entry is NaN.
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Median ( this |
Estimates the pointwise median value from the vectors.
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Minimum ( this |
Returns the pointwise smallest value of the vectors Returns NaN if data is empty or any entry is NaN.
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Minimum ( this |
Returns the pointwise smallest value from the vectors. Returns NaN if data is empty or any entry is NaN.
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OrderStatistic ( this |
Returns the pointwise order statistic (order 1..N) from the vectors.
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Percentile ( this |
Estimates the pointwise p-Percentile value from the vectors. If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
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PopulationKurtosis ( this |
Evaluates the pointwise kurtosis from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1). Returns NaN if data has less than three entries or if any entry is NaN.
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PopulationSkewness ( this |
Evaluates the pointwise skewness from the full population. Does not use a normalizer and would thus be biased if applied to a subset (type 1). Returns NaN if data has less than two entries or if any entry is NaN.
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PopulationStandardDeviation ( this |
Evaluates the pointwise population standard deviation from the full population provided as vectors. On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN.
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PopulationVariance ( this |
Evaluates the pointwise population variance from the full population provided as vectors. On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset. Returns NaN if data is empty or if any entry is NaN.
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Quantile ( this |
Estimates the pointwise tau-th quantile from the vectors. The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8). R-8, SciPy-(1/3,1/3): Linear interpolation of the approximate medians for order statistics. When tau < (2/3) / (N + 1/3), use x1. When tau >= (N - 1/3) / (N + 1/3), use xN. |
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QuantileCustom ( this |
Estimates the pointwise tau-th quantile from the vectors. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
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QuantileCustom ( this |
Estimates the pointwise tau-th quantile from the vectors. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified by 4 parameters a, b, c and d, consistent with Mathematica.
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QuantileRank ( this |
Estimates the pointwise quantile tau from the vectors. The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
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RootMeanSquare ( this |
Estimates the pointwise root mean square (RMS) also known as quadratic mean from the vectors. Returns NaN if data is empty or any entry is NaN.
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Skewness ( this |
Estimates the pointwise unbiased population skewness from the provided samples. Uses a normalizer (Bessel's correction; type 2). Returns NaN if data has less than three entries or if any entry is NaN.
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StandardDeviation ( this |
Estimates the pointwise unbiased population standard deviation from the provided samples as vectors. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN.
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UpperQuartile ( this |
Estimates the pointwise third quartile value from the vectors. Approximately median-unbiased regardless of the sample distribution (R8).
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Variance ( this |
Estimates the pointwise unbiased population variance from the provided samples as vectors. On a dataset of size N will use an N-1 normalizer (Bessel's correction). Returns NaN if data has less than two entries or if any entry is NaN.
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public static EmpiricalCDF ( this |
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data | this |
Vector array, where all vectors have the same length. |
x | double | The value where to estimate the CDF at. |
Résultat | Vector |
public static EmpiricalInvCDF ( this |
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data | this |
Vector array, where all vectors have the same length. |
tau | double | Quantile selector, between 0.0 and 1.0 (inclusive). |
Résultat | Vector |
public static Entropy ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static InterquartileRange ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Kurtosis ( this |
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samples | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static LowerQuartile ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Maximum ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Maximum ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Mean ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Median ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Minimum ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Minimum ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static OrderStatistic ( this |
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data | this |
Vector array, where all vectors have the same length. |
order | int | One-based order of the statistic, must be between 1 and N (inclusive). |
Résultat | Vector |
public static Percentile ( this |
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data | this |
Vector array, where all vectors have the same length. |
p | int | Percentile selector, between 0 and 100 (inclusive). |
Résultat | Vector |
public static PopulationKurtosis ( this |
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population | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static PopulationSkewness ( this |
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population | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static PopulationStandardDeviation ( this |
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population | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static PopulationVariance ( this |
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population | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Quantile ( this |
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data | this |
Vector array, where all vectors have the same length. |
tau | double | Quantile selector, between 0.0 and 1.0 (inclusive). |
Résultat | Vector |
public static QuantileCustom ( this |
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data | this |
Vector array, where all vectors have the same length. |
tau | double | Quantile selector, between 0.0 and 1.0 (inclusive) |
definition | QuantileDefinition | Quantile definition, to choose what product/definition it should be consistent with |
Résultat | Vector |
public static QuantileCustom ( this |
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data | this |
Vector array, where all vectors have the same length. |
tau | double | Quantile selector, between 0.0 and 1.0 (inclusive) |
a | double | a-parameter |
b | double | b-parameter |
c | double | c-parameter |
d | double | d-parameter |
Résultat | Vector |
public static QuantileRank ( this |
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data | this |
Vector array, where all vectors have the same length. |
x | double | Quantile value. |
definition | RankDefinition | Rank definition, to choose how ties should be handled and what product/definition it should be consistent with |
Résultat | Vector |
public static RootMeanSquare ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Skewness ( this |
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samples | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static StandardDeviation ( this |
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samples | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static UpperQuartile ( this |
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data | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |
public static Variance ( this |
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samples | this |
Vector array, where all vectors have the same length. |
Résultat | Vector |