Bases: object
This class implements non-parametric kernel density estimation.
Methods
run(X[, specs, nSamples, counts, a, bounds]) | Primary execution point. |
Primary execution point. Run either standard KDE or class-membership based KDE. If any of the class-membership based KDE arguments are set, it will be run instead of standard KDE.
Parameters : | X : array_like
nSamples : int
specs : qikify.models.Specs, optional
counts : dict, optional
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Bases: object
Methods
constructS(X, gnd[, k, t, bLDA, bSelfConnected]) | |
run(Xin, gnd) | Run Laplacian Score Feature Selection. |
threshold(T_L) |
Run Laplacian Score Feature Selection.
Note
Eventually, it’d be nice to maintain col names with Xin so that we can add a plot method to plot scores vs. column names.
Parameters : | Xin : array_like
gnd : array_like
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Notes
This code is based on the definition from the paper [R1]:
[R1] | He, X. and Cai, D. and Niyogi, P., “Laplacian Score for Feature Selection”, NIPS 2005. |
Bases: object
Ordinary least squares multivariate regression.
Methods
JB() | Calculate residual skewness, kurtosis, and do the JB test for normality |
computeStatistics() | |
dw() | Calculates the Durbin-Waston statistic |
ll() | Calculate model log-likelihood and two information criteria |
omni() | Omnibus test for normality |
train(X, y[, useQR, addConstant]) | Solve y = Xb. |
Bases: object
Qikify feature selection library. Doesn’t do much yet; right now only implements correlation coefficient-based feature selection.
Methods
computeCorrCoefs(X, y) | Returns the correlation coefficients between X and y, |
run(X, y[, n_features, intercept, method]) | Do feature selection on the basis of correlation coefficients. |
Returns the correlation coefficients between X and y, along with the arg-sorted indices of ranked most-correlated X-to-y vars.
Do feature selection on the basis of correlation coefficients.
Parameters : | X : numpy array of shape [n_samples,n_features]
y : numpy array of shape [n_samples]
n_features : int, optional
intercept : bool, optional
method : string, optional
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Returns : | features : The X column indices to retain. |
Notes
We typically exclude the first column since it is the intercept all-constant column.
bilinear interpolation of z over 2d surface {x,y}