Machine learning tools in JavaScript
JavaScript
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Support the dependencies of mljs/ml
Cross validation utility for mljs classifiers
A factory for kernel functions
Random forest for classification and regression
tiny binary search function with comparators
Get the maximum value in an array
Get the average value in an array
Get the median value in an array
Get the minimum value in an array
Rescale an array into a range
Get the most repeated value in an array
Get the standard deviation in an array
Get the average value in an array
Get the closest point for a specific abscissa value
Normalize the values of a given array
Get the variance in an array
Find the maximum ordinate value in a range of abscissas of a {x:[], y:[]}
Sort a set of point based on the abscissas values
Filter a XY array based on x values
CART decision tree algorithm
Distance and similarity functions to compare vectors
Bit-array operations in JavaScript
Confusion matrix for supervised classification
Global Spectra Deconvolution
Open addressing hash table
Merge abscissa values on similar ordinates and weight the group of abscissa
Create an array with sequential numbers
Merge abscissa values if the ordinate value is in a list of centroids
K-Means clustering
Compute a distance/similarity matrix
Curve fitting method in javascript
Matrix manipulation and computation library
Get the closest point for a specific abscissa value
k-nearest neighbors algorithm
Naive bayes library
Principal component analysis
non-negative Generalized Morphological Component Analysis
Calculate covariance
Regression algorithms
Partial least squares library
Choose randomly from a selection of elements
Savitzky–Golay filter
Function to sum the y values if the x values are equals. The x array has to be ordered.
Sparse matrix library
Merge abscissa values on similar ordinates and keeps the abscissa with bigger ordinate value
Fast Combinatorial Non-negative Least Squares
feedforward neural networks library
Hierarchical clustering algorithms
Function to fill an array in Javascript
Library providing tools to evaluate the performance of a classifier using for example ROC curves
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