Gonum stat

Package stat is a statistics package for the Go language.

Documentation ¶

Overview ¶

Package stat provides generalized statistical functions.

Constants ¶

This section is empty.

Variables ¶

This section is empty.

Functions ¶

func Bhattacharyya ¶

func Bhattacharyya(p, q []float64) float64

Bhattacharyya computes the distance between the probability distributions p and q given by:

-\ln ( \sum_i \sqrt{p_i q_i} )


The lengths of p and q must be equal. It is assumed that p and q sum to 1.

func BivariateMoment ¶

func BivariateMoment(r, s float64, x, y, weights []float64) float64

BivariateMoment computes the weighted mixed moment between the samples x and y.

E[(x - μ_x)^r*(y - μ_y)^s]


No degrees of freedom correction is done. The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

func CDF ¶

func CDF(q float64, c CumulantKind, x, weights []float64) float64

CDF returns the empirical cumulative distribution function value of x, that is the fraction of the samples less than or equal to q. The exact behavior is determined by the CumulantKind. CDF is theoretically the inverse of the Quantile function, though it may not be the actual inverse for all values q and CumulantKinds.

The x data must be sorted in increasing order. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

CumulantKind behaviors:

- Empirical: Returns the lowest fraction for which q is greater than or equal
to that fraction of samples


func ChiSquare ¶

func ChiSquare(obs, exp []float64) float64

ChiSquare computes the chi-square distance between the observed frequencies 'obs' and expected frequencies 'exp' given by:

\sum_i (obs_i-exp_i)^2 / exp_i


The lengths of obs and exp must be equal.

func CircularMean ¶

func CircularMean(x, weights []float64) float64

CircularMean returns the circular mean of the dataset.

atan2(\sum_i w_i * sin(alpha_i), \sum_i w_i * cos(alpha_i))


If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

The circular mean is 1.37037.


func Correlation ¶

func Correlation(x, y, weights []float64) float64

Correlation returns the weighted correlation between the samples of x and y with the given means.

sum_i {w_i (x_i - meanX) * (y_i - meanY)} / (stdX * stdY)


The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

Correlation computes the degree to which two datasets move together
about their mean. For example, x and y above move similarly.
Correlation is 0.59915


func CorrelationMatrix ¶

func CorrelationMatrix(dst *mat.SymDense, x mat.Matrix, weights []float64)

CorrelationMatrix returns the correlation matrix calculated from a matrix of data, x, using a two-pass algorithm. The result is stored in dst.

If weights is not nil the weighted correlation of x is calculated. weights must have length equal to the number of rows in input data matrix and must not contain negative elements. The dst matrix must either be empty or have the same number of columns as the input data matrix.

func Covariance ¶

func Covariance(x, y, weights []float64) float64

Covariance returns the weighted covariance between the samples of x and y.

sum_i {w_i (x_i - meanX) * (y_i - meanY)} / (sum_j {w_j} - 1)


The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

Covariance computes the degree to which datasets move together
Cov = 13.8000
If datasets move perfectly together, the variance equals the covariance
Cov2 is 37.7000, VarX is 37.7000


func CovarianceMatrix ¶

func CovarianceMatrix(dst *mat.SymDense, x mat.Matrix, weights []float64)

CovarianceMatrix calculates the covariance matrix (also known as the variance-covariance matrix) calculated from a matrix of data, x, using a two-pass algorithm. The result is stored in dst.

If weights is not nil the weighted covariance of x is calculated. weights must have length equal to the number of rows in input data matrix and must not contain negative elements. The dst matrix must either be empty or have the same number of columns as the input data matrix.

func CrossEntropy ¶

func CrossEntropy(p, q []float64) float64

CrossEntropy computes the cross-entropy between the two distributions specified in p and q.

func Entropy ¶

func Entropy(p []float64) float64

Entropy computes the Shannon entropy of a distribution or the distance between two distributions. The natural logarithm is used.

- sum_i (p_i * log_e(p_i))

Example
Output:

Entropy is a measure of the amount of uncertainty in a distribution
The second bin of p is very likely to occur. It's entropy is 0.6247
The distribution of q is more spread out. It's entropy is 1.3195
Adding buckets with zero probability does not change the entropy.
The entropy of r is: 1.2206
A distribution with no uncertainty has entropy 0.0000


func ExKurtosis ¶

func ExKurtosis(x, weights []float64) float64

ExKurtosis returns the population excess kurtosis of the sample. The kurtosis is defined by the 4th moment of the mean divided by the squared variance. The excess kurtosis subtracts 3.0 so that the excess kurtosis of the normal distribution is zero. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

Kurtosis is a measure of the 'peakedness' of a distribution, and the
excess kurtosis is the kurtosis above or below that of the standard normal
distribution
ExKurtosis = -5.41200
Weighted ExKurtosis is -0.6779


func GeometricMean ¶

func GeometricMean(x, weights []float64) float64

GeometricMean returns the weighted geometric mean of the dataset

\prod_i {x_i ^ w_i}


This only applies with positive x and positive weights. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

The arithmetic mean is 10.1667, but the geometric mean is 8.7637.
The exponential of the mean of the logs is 8.7637


func HarmonicMean ¶

func HarmonicMean(x, weights []float64) float64

HarmonicMean returns the weighted harmonic mean of the dataset

\sum_i {w_i} / ( sum_i {w_i / x_i} )


This only applies with positive x and positive weights. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

The arithmetic mean is 10.16667, but the harmonic mean is 6.8354.


func Hellinger ¶

func Hellinger(p, q []float64) float64

Hellinger computes the distance between the probability distributions p and q given by:

\sqrt{ 1 - \sum_i \sqrt{p_i q_i} }


The lengths of p and q must be equal. It is assumed that p and q sum to 1.

func Histogram ¶

func Histogram(count, dividers, x, weights []float64) []float64

Histogram sums up the weighted number of data points in each bin. The weight of data point x[i] will be placed into count[j] if dividers[j] <= x < dividers[j+1]. The "span" function in the floats package can assist with bin creation.

The following conditions on the inputs apply:

- The count variable must either be nil or have length of one less than dividers.
- The values in dividers must be sorted (use the sort package).
- The x values must be sorted.
- If weights is nil then all of the weights are 1.
- If weights is not nil, then len(x) must equal len(weights).

Example
Output:

Histogram counts the amount of data in the bins specified by
the dividers. In this data set, there are 7 data points less than 7 (between dividers[0]
and dividers[1]), 12 data points between 7 and 20 (dividers[1] and dividers[2]),
and 0 data points above 1000. Since dividers has length 5, there will be 4 bins.
Hist = [7 12 72 10]

For ease, the floats Span function can be used to set the dividers
Hist = [11 10 10 10 10 10 10 10 10 10]

Histogram also works with weighted data, and allows reusing of
the count field in order to avoid extra garbage
Weighted Hist = [66 165 265 365 465 565 665 765 865 965]


func JensenShannon ¶

func JensenShannon(p, q []float64) float64

JensenShannon computes the JensenShannon divergence between the distributions p and q. The Jensen-Shannon divergence is defined as

m = 0.5 * (p + q)
JS(p, q) = 0.5 ( KL(p, m) + KL(q, m) )


Unlike Kullback-Liebler, the Jensen-Shannon distance is symmetric. The value is between 0 and ln(2).

func Kendall ¶

func Kendall(x, y, weights []float64) float64

Kendall returns the weighted Tau-a Kendall correlation between the samples of x and y. The Kendall correlation measures the quantity of concordant and discordant pairs of numbers. If weights are specified then each pair is weighted by weights[i] * weights[j] and the final sum is normalized to stay between -1 and 1. The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

Kendall correlation computes the number of ordered pairs
between two datasets.
Kendall correlation is 0.25000


func KolmogorovSmirnov ¶

func KolmogorovSmirnov(x, xWeights, y, yWeights []float64) float64

KolmogorovSmirnov computes the largest distance between two empirical CDFs. Each dataset x and y consists of sample locations and counts, xWeights and yWeights, respectively.

x and y may have different lengths, though len(x) must equal len(xWeights), and len(y) must equal len(yWeights). Both x and y must be sorted.

Special cases are:

= 0 if len(x) == len(y) == 0
= 1 if len(x) == 0, len(y) != 0 or len(x) != 0 and len(y) == 0


func KullbackLeibler ¶

func KullbackLeibler(p, q []float64) float64

KullbackLeibler computes the Kullback-Leibler distance between the distributions p and q. The natural logarithm is used.

sum_i(p_i * log(p_i / q_i))


Note that the Kullback-Leibler distance is not symmetric; KullbackLeibler(p,q) != KullbackLeibler(q,p)

Example
Output:



func LinearRegression ¶

func LinearRegression(x, y, weights []float64, origin bool) (alpha, beta float64)

LinearRegression computes the best-fit line

y = alpha + beta*x


to the data in x and y with the given weights. If origin is true, the regression is forced to pass through the origin.

Specifically, LinearRegression computes the values of alpha and beta such that the total residual

\sum_i w[i]*(y[i] - alpha - beta*x[i])^2


is minimized. If origin is true, then alpha is forced to be zero.

The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

Estimated slope is:  0.988572424633503
Estimated offset is: 3.0001541344029676
R^2: 0.9999991095061128


func Mahalanobis ¶

func Mahalanobis(x, y mat.Vector, chol *mat.Cholesky) float64

Mahalanobis computes the Mahalanobis distance

D = sqrt((x-y)ᵀ * Σ^-1 * (x-y))


between the column vectors x and y given the cholesky decomposition of Σ. Mahalanobis returns NaN if the linear solve fails.

func Mean ¶

func Mean(x, weights []float64) float64

Mean computes the weighted mean of the data set.

sum_i {w_i * x_i} / sum_i {w_i}


If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

Example
Output:

The mean of the samples is 3.5500
The weighted mean of the samples is 1.9000
The mean of x2 is 1.9000
The weights act as if there were more samples of that number


func MeanStdDev ¶

func MeanStdDev(x, weights []float64) (mean, std float64)

MeanStdDev returns the sample mean and unbiased standard deviation When weights sum to 1 or less, a biased variance estimator should be used.

func MeanVariance ¶

func MeanVariance(x, weights []float64) (mean, variance float64)

MeanVariance computes the sample mean and unbiased variance, where the mean and variance are

\sum_i w_i * x_i / (sum_i w_i)
\sum_i w_i (x_i - mean)^2 / (sum_i w_i - 1)


respectively. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights). When weights sum to 1 or less, a biased variance estimator should be used.

func Mode ¶

func Mode(x, weights []float64) (val float64, count float64)

Mode returns the most common value in the dataset specified by x and the given weights. Strict float64 equality is used when comparing values, so users should take caution. If several values are the mode, any of them may be returned.

func Moment ¶

func Moment(moment float64, x, weights []float64) float64

Moment computes the weighted n^th moment of the samples,

E[(x - μ)^N]


No degrees of freedom correction is done. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

func MomentAbout(moment float64, x []float64, mean float64, weights []float64) float64

MomentAbout computes the weighted n^th weighted moment of the samples about the given mean \mu,

E[(x - μ)^N]


No degrees of freedom correction is done. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

func Quantile ¶

func Quantile(p float64, c CumulantKind, x, weights []float64) float64

Quantile returns the sample of x such that x is greater than or equal to the fraction p of samples. The exact behavior is determined by the CumulantKind, and p should be a number between 0 and 1. Quantile is theoretically the inverse of the CDF function, though it may not be the actual inverse for all values p and CumulantKinds.

The x data must be sorted in increasing order. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

CumulantKind behaviors:

- Empirical: Returns the lowest value q for which q is greater than or equal
to the fraction p of samples
- LinInterp: Returns the linearly interpolated value


func RNoughtSquared ¶

func RNoughtSquared(x, y, weights []float64, beta float64) float64

RNoughtSquared returns the coefficient of determination defined as

R₀^2 = \sum_i w[i]*(beta*x[i])^2 / \sum_i w[i]*y[i]^2


for the line

y = beta*x


and the data in x and y with the given weights. RNoughtSquared should only be used for best-fit lines regressed through the origin.

The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

func ROC ¶

func ROC(cutoffs, y []float64, classes []bool, weights []float64) (tpr, fpr, thresh []float64)

ROC returns paired false positive rate (FPR) and true positive rate (TPR) values corresponding to cutoff points on the receiver operator characteristic (ROC) curve obtained when y is treated as a binary classifier for classes with weights. The cutoff thresholds used to calculate the ROC are returned in thresh such that tpr[i] and fpr[i] are the true and false positive rates for y >= thresh[i].

The input y and cutoffs must be sorted, and values in y must correspond to values in classes and weights. SortWeightedLabeled can be used to sort y together with classes and weights.

For a given cutoff value, observations corresponding to entries in y greater than the cutoff value are classified as false, while those less than or equal to the cutoff value are classified as true. These assigned class labels are compared with the true values in the classes slice and used to calculate the FPR and TPR.

If weights is nil, all weights are treated as 1.

If cutoffs is nil or empty, all possible cutoffs are calculated, resulting in fpr and tpr having length one greater than the number of unique values in y. Otherwise fpr and tpr will be returned with the same length as cutoffs. floats.Span can be used to generate equally spaced cutoffs.

Example (AUC)
Output:

true  positive rate: [0 0 0.5 0.5 1]
false positive rate: [0 0.5 0.5 1 1]
auc: 0.25

Example (EquallySpacedCutoffs)
Output:

true  positive rate: [0 0.333 0.333 0.583 0.583 0.583 0.667 0.667 1]
false positive rate: [0 0 0 0 1 1 1 1 1]

Example (KnownCutoffs)
Output:

true  positive rate: [0.875 0.875 1]
false positive rate: [0.6 0.6 1]

Example (Threshold)
Output:

true  positive rate: [0 0.5 0.5 1 1]
false positive rate: [0 0 0.5 0.5 1]
cutoff thresholds: [+Inf 0.8 0.4 0.35 0.1]

Example (Unsorted)
Output:

true  positive rate: [0 0.25 0.5 0.875 0.875 1 1]
false positive rate: [0 0 0 0 0.6 0.6 1]

Example (Unweighted)
Output:

true  positive rate: [0 0.25 0.5 0.75 0.75 1 1]
false positive rate: [0 0 0 0 0.5 0.5 1]

Example (Weighted)
Output:

true  positive rate: [0 0.25 0.5 0.875 0.875 1 1]
false positive rate: [0 0 0 0 0.6 0.6 1]


func RSquared ¶

func RSquared(x, y, weights []float64, alpha, beta float64) float64

RSquared returns the coefficient of determination defined as

R^2 = 1 - \sum_i w[i]*(y[i] - alpha - beta*x[i])^2 / \sum_i w[i]*(y[i] - mean(y))^2


for the line

y = alpha + beta*x


and the data in x and y with the given weights.

The lengths of x and y must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights).

func RSquaredFrom ¶

func RSquaredFrom(estimates, values, weights []float64) float64

RSquaredFrom returns the coefficient of determination defined as

R^2 = 1 - \sum_i w[i]*(estimate[i] - value[i])^2 / \sum_i w[i]*(value[i] - mean(values))^2


and the data in estimates and values with the given weights.

The lengths of estimates and values must be equal. If weights is nil then all of the weights are 1. If weights is not nil, then len(values) must equal len(weights).

func Skew ¶

func Skew(x, weights []float64) float64

Skew computes the skewness of the sample data. If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights). When weights sum to 1 or less, a biased variance estimator should be used.

func SortWeighted ¶

func SortWeighted(x, weights []float64)

SortWeighted rearranges the data in x along with their corresponding weights so that the x data are sorted. The data is sorted in place. Weights may be nil, but if weights is non-nil then it must have the same length as x.

func SortWeightedLabeled ¶

func SortWeightedLabeled(x []float64, labels []bool, weights []float64)

SortWeightedLabeled rearranges the data in x along with their corresponding weights and boolean labels so that the x data are sorted. The data is sorted in place. Weights and labels may be nil, if either is non-nil it must have the same length as x.

func StdDev ¶

func StdDev(x, weights []float64) float64

StdDev returns the sample standard deviation.

Example
Output:

The standard deviation of the samples is 8.8034
The weighted standard deviation of the samples is 10.5733


func StdErr ¶

func StdErr(std, sampleSize float64) float64

StdErr returns the standard error in the mean with the given values.

Example
Output:

The standard deviation is 10.5733 and there are 18 samples, so the mean
is likely 4.1667 ± 2.4921.


func StdScore ¶

func StdScore(x, mean, std float64) float64

StdScore returns the standard score (a.k.a. z-score, z-value) for the value x with the given mean and standard deviation, i.e.

(x - mean) / std


func Variance ¶

func Variance(x, weights []float64) float64

Variance computes the unbiased weighted sample variance:

\sum_i w_i (x_i - mean)^2 / (sum_i w_i - 1)


If weights is nil then all of the weights are 1. If weights is not nil, then len(x) must equal len(weights). When weights sum to 1 or less, a biased variance estimator should be used.

Example
Output:

The variance of the samples is 77.5000
The weighted variance of the samples is 111.7941


Types ¶

type CC ¶

type CC struct {
// contains filtered or unexported fields
}

CC is a type for computing the canonical correlations of a pair of matrices. The results of the canonical correlation analysis are only valid if the call to CanonicalCorrelations was successful.

Example
Output:

corRaw = ⎡-0.2192   0.3527   0.5828  -0.3883⎤
⎢-0.3917   0.6448   0.7208  -0.4837⎥
⎢-0.3022   0.7315   0.6680  -0.4273⎥
⎢ 0.2052  -0.7479  -0.5344   0.2499⎥
⎢-0.2098   0.4560   0.9102  -0.3816⎥
⎢-0.3555   0.2615   0.4609  -0.5078⎥
⎣ 0.1281  -0.2735  -0.4418   0.3335⎦

corSph = ⎡ 0.0118   0.0525   0.2300  -0.1363⎤
⎢-0.1810   0.3213   0.3814  -0.1412⎥
⎢ 0.0166   0.2241   0.0104  -0.2235⎥
⎢ 0.0346  -0.5481  -0.0034  -0.1994⎥
⎢ 0.0303  -0.0956   0.7152   0.2039⎥
⎢-0.0298  -0.0022   0.0739  -0.3703⎥
⎣-0.1226  -0.0746  -0.3899   0.1541⎦

ccors = [0.9451 0.6787 0.5714 0.2010]

pVecs = ⎡-0.2574   0.0158   0.2122  -0.0946⎤
⎢-0.4837   0.3837   0.1474   0.6597⎥
⎢-0.0801   0.3494   0.3287  -0.2862⎥
⎢ 0.1278  -0.7337   0.4851   0.2248⎥
⎢-0.6969  -0.4342  -0.3603   0.0291⎥
⎢-0.0991   0.0503   0.6384   0.1022⎥
⎣ 0.4260   0.0323  -0.2290   0.6419⎦

qVecs = ⎡ 0.0182  -0.1583  -0.0067  -0.9872⎤
⎢-0.2348   0.9483  -0.1462  -0.1554⎥
⎢-0.9701  -0.2406  -0.0252   0.0209⎥
⎣ 0.0593  -0.1330  -0.9889   0.0291⎦

phiVs = ⎡-0.0027   0.0093   0.0490  -0.0155⎤
⎢-0.0429  -0.0242   0.0361   0.1839⎥
⎢-1.2248   5.6031   5.8094  -4.7927⎥
⎢-0.0044  -0.3424   0.4470   0.1150⎥
⎢-0.0742  -0.1193  -0.1116   0.0022⎥
⎢-0.0233   0.1046   0.3853  -0.0161⎥
⎣ 0.0001   0.0005  -0.0030   0.0082⎦

psiVs = ⎡ 0.0302  -0.3002   0.0878  -1.9583⎤
⎢-0.0065   0.0392  -0.0118  -0.0061⎥
⎢-0.0052  -0.0046  -0.0023   0.0008⎥
⎣ 0.0020   0.0037  -0.1293   0.1038⎦


func (*CC) CanonicalCorrelations ¶

func (c *CC) CanonicalCorrelations(x, y mat.Matrix, weights []float64) error

CanonicalCorrelations performs a canonical correlation analysis of the input data x and y, columns of which should be interpretable as two sets of measurements on the same observations (rows). These observations are optionally weighted by weights. The result of the analysis is stored in the receiver if the analysis is successful.

Canonical correlation analysis finds associations between two sets of variables on the same observations by finding linear combinations of the two sphered datasets that maximize the correlation between them.

Some notation: let Xc and Yc denote the centered input data matrices x and y (column means subtracted from each column), let Sx and Sy denote the sample covariance matrices within x and y respectively, and let Sxy denote the covariance matrix between x and y. The sphered data can then be expressed as Xc * Sx^{-1/2} and Yc * Sy^{-1/2} respectively, and the correlation matrix between the sphered data is called the canonical correlation matrix, Sx^{-1/2} * Sxy * Sy^{-1/2}. In cases where S^{-1/2} is ambiguous for some covariance matrix S, S^{-1/2} is taken to be E * D^{-1/2} * Eᵀ where S can be eigendecomposed as S = E * D * Eᵀ.

The canonical correlations are the correlations between the corresponding pairs of canonical variables and can be obtained with c.Corrs(). Canonical variables can be obtained by projecting the sphered data into the left and right eigenvectors of the canonical correlation matrix, and these eigenvectors can be obtained with c.Left(m, true) and c.Right(m, true) respectively. The canonical variables can also be obtained directly from the centered raw data by using the back-transformed eigenvectors which can be obtained with c.Left(m, false) and c.Right(m, false) respectively.

The first pair of left and right eigenvectors of the canonical correlation matrix can be interpreted as directions into which the respective sphered data can be projected such that the correlation between the two projections is maximized. The second pair and onwards solve the same optimization but under the constraint that they are uncorrelated (orthogonal in sphered space) to previous projections.

CanonicalCorrelations will panic if the inputs x and y do not have the same number of rows.

The slice weights is used to weight the observations. If weights is nil, each weight is considered to have a value of one, otherwise the length of weights must match the number of observations (rows of both x and y) or CanonicalCorrelations will panic.

More details can be found at https://en.wikipedia.org/wiki/Canonical_correlation or in Chapter 3 of Koch, Inge. Analysis of multivariate and high-dimensional data. Vol. 32. Cambridge University Press, 2013. ISBN: 9780521887939

func (*CC) CorrsTo ¶

func (c *CC) CorrsTo(dst []float64) []float64

CorrsTo returns the canonical correlations, using dst if it is not nil. If dst is not nil and len(dst) does not match the number of columns in the y input matrix, Corrs will panic.

func (*CC) LeftTo ¶

func (c *CC) LeftTo(dst *mat.Dense, spheredSpace bool)

LeftTo returns the left eigenvectors of the canonical correlation matrix if spheredSpace is true. If spheredSpace is false it returns these eigenvectors back-transformed to the original data space.

If dst is empty, LeftTo will resize dst to be xd×yd. When dst is non-empty, LeftTo will panic if dst is not xd×yd. LeftTo will also panic if the receiver does not contain a successful CC.

func (*CC) RightTo ¶

func (c *CC) RightTo(dst *mat.Dense, spheredSpace bool)

RightTo returns the right eigenvectors of the canonical correlation matrix if spheredSpace is true. If spheredSpace is false it returns these eigenvectors back-transformed to the original data space.

If dst is empty, RightTo will resize dst to be yd×yd. When dst is non-empty, RightTo will panic if dst is not yd×yd. RightTo will also panic if the receiver does not contain a successful CC.

type CumulantKind ¶

type CumulantKind int

CumulantKind specifies the behavior for calculating the empirical CDF or Quantile

const (
// Empirical treats the distribution as the actual empirical distribution.
Empirical CumulantKind = 1
// LinInterp linearly interpolates the empirical distribution between sample values, with a flat extrapolation.
LinInterp CumulantKind = 4
)

List of supported CumulantKind values for the Quantile function. Constant values should match the R nomenclature. See https://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population

type PC ¶

type PC struct {
// contains filtered or unexported fields
}

PC is a type for computing and extracting the principal components of a matrix. The results of the principal components analysis are only valid if the call to PrincipalComponents was successful.

Example
Output:

variances = [0.1666 0.0207 0.0079 0.0019]

proj = ⎡-6.1686   1.4659⎤
⎢-5.6767   1.6459⎥
⎢-5.6699   1.3642⎥
⎢-5.5643   1.3816⎥
⎢-6.1734   1.3309⎥
⎢-6.7278   1.4021⎥
⎢-5.7743   1.1498⎥
⎢-6.0466   1.4714⎥
⎢-5.2709   1.3570⎥
⎣-5.7533   1.6207⎦


func (*PC) PrincipalComponents ¶

func (c *PC) PrincipalComponents(a mat.Matrix, weights []float64) (ok bool)

PrincipalComponents performs a weighted principal components analysis on the matrix of the input data which is represented as an n×d matrix a where each row is an observation and each column is a variable.

PrincipalComponents centers the variables but does not scale the variance.

The weights slice is used to weight the observations. If weights is nil, each weight is considered to have a value of one, otherwise the length of weights must match the number of observations or PrincipalComponents will panic.

PrincipalComponents returns whether the analysis was successful.

func (*PC) VarsTo ¶

func (c *PC) VarsTo(dst []float64) []float64

VarsTo returns the column variances of the principal component scores, b * vecs, where b is a matrix with centered columns. Variances are returned in descending order. If dst is not nil it is used to store the variances and returned. Vars will panic if the receiver has not successfully performed a principal components analysis or dst is not nil and the length of dst is not min(n, d).

func (*PC) VectorsTo ¶

func (c *PC) VectorsTo(dst *mat.Dense)

VectorsTo returns the component direction vectors of a principal components analysis. The vectors are returned in the columns of a d×min(n, d) matrix.

If dst is empty, VectorsTo will resize dst to be d×min(n, d). When dst is non-empty, VectorsTo will panic if dst is not d×min(n, d). VectorsTo will also panic if the receiver does not contain a successful PC.

Directories ¶

Path Synopsis
Package combin implements routines involving combinatorics (permutations, combinations, etc.).
Package combin implements routines involving combinatorics (permutations, combinations, etc.).
Package distmat provides probability distributions over matrices.
Package distmat provides probability distributions over matrices.
Package distmv provides multivariate random distribution types.
Package distmv provides multivariate random distribution types.
Package distuv provides univariate random distribution types.
Package distuv provides univariate random distribution types.
Package mds provides multidimensional scaling functions.
Package mds provides multidimensional scaling functions.
Package samplemv implements advanced sampling routines from explicit and implicit probability distributions.
Package samplemv implements advanced sampling routines from explicit and implicit probability distributions.
Package sampleuv implements advanced sampling routines from explicit and implicit probability distributions.
Package sampleuv implements advanced sampling routines from explicit and implicit probability distributions.
Package spatial provides spatial statistical functions.
Package spatial provides spatial statistical functions.