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var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create a new Ridge Regression object
var regmod = new analytics.RidgeReg();
// create the test matrix and vector
var X = new la.Matrix([[1, 2], [1, -1]]);
var y = new la.Vector([3, 3]);
// fit the model with X and y
regmod.fit(X, y);
// create an output stream object and save the model
var fout = fs.openWrite('regmod_example.bin');
regmod.save(fout);
fout.close();
// create a new Ridge Regression model by loading the model
var fin = fs.openRead('regmod_example.bin');
var regmod2 = new analytics.RidgeReg(fin);
});
});
var la = require('qminer').la;
var fs = require('qminer').fs;
// create a new SVC object
var SVC = new analytics.SVC();
// create the matrix containing the input features and the input vector for each matrix column.
var matrix = new la.Matrix([[1, 0, -1, 0], [0, 1, 0, -1]]);
var vec = new la.Vector([1, 0, -1, -2]);
// fit the model
SVC.fit(matrix, vec);
// create output stream
var fout = fs.openWrite('svc_example.bin');
// save SVC object (model and parameters) to output stream and close it
SVC.save(fout);
fout.close();
// create input stream
var fin = fs.openRead('svc_example.bin');
// create a SVC object that loads the model and parameters from input stream
var SVC2 = new analytics.SVC(fin);
});
});
var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create a Neural Networks model
var nnet = new analytics.NNet({ layout: [2, 3, 4] });
// create the matrices for the fitting of the model
var matIn = new la.Matrix([[1, 0], [0, 1]]);
var matOut = new la.Matrix([[1, 1], [1, 2], [-1, 8], [-3, -3]]);
// fit the model
nnet.fit(matIn, matOut);
// create an output stream object and save the model
var fout = fs.openWrite('nnet_example.bin');
nnet.save(fout);
fout.close();
// load the Neural Network model from the binary
var fin = fs.openRead('nnet_example.bin');
var nnet2 = new analytics.NNet(fin);
});
});
it("should make test number 97", function () {
// import fs module
var fs = require('qminer').fs;
var la = require('qminer').la;
// create an empty vector
var vec = new la.IntVector();
// open a read stream
var fin = fs.openRead('vec.dat');
// load the matrix
vec.loadascii(fin);
});
});
var fs = require('qminer').fs;
// create the logistic regression model
var logreg = new analytics.LogReg();
// create the input matrix and vector for fitting the model
var mat = new la.Matrix([[1, 0, -1, 0], [0, 1, 0, -1]]);
var vec = new la.Vector([1, 0, -1, -2]);
// if openblas is used, fit the model
if (require('qminer').flags.blas) {
logreg.fit(mat, vec);
};
// create an output stream object and save the model
var fout = fs.openWrite('logreg_example.bin');
logreg.save(fout);
fout.close();
// create input stream
var fin = fs.openRead('logreg_example.bin');
// create a Logistic Regression object that loads the model and parameters from input stream
var logreg2 = new analytics.LogReg(fin);
});
});
it("should make test number 75", function () {
// import modules
var analytics = require('qminer').analytics;
var fs = require('qminer').fs;
// create a MDS instance
var mds = new analytics.MDS({ iter: 200, MaxStep: 10 });
// create the file output stream
var fout = new fs.openWrite('MDS.bin');
// save the MDS instance
mds.save(fout);
fout.close();
// load the MDS instance
var fin = fs.openRead('MDS.bin');
var mds2 = new analytics.MDS(fin);
});
});
var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10 });
// create a matrix and vector for the model
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
// create the model by fitting the values
SVR.fit(matrix, vector);
// save the model in a binary file
var fout = fs.openWrite('svr_example.bin');
SVR.save(fout);
fout.close();
// construct a SVR model by loading from the binary file
var fin = fs.openRead('svr_example.bin');
var SVR2 = new analytics.SVR()
});
});
it("should make test number 1", function () {
// import module
var fs = require('qminer').fs;
// open file in write mode
var fout = fs.openWrite('file.txt');
// write sync and close
fout.writeLine('example text');
fout.close();
// open file in read mode
var fin = fs.openRead('file.txt');
// read a line
var str = fin.readLine();
});
});
// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create a new NearestNeighborAD object
var neighbor = new analytics.NearestNeighborAD();
// create a new sparse matrix
var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]], [[0, 0], [1, 1]]]);
// fit the model with the matrix
neighbor.fit(matrix);
// create an output stream object and save the model
var fout = fs.openWrite('neighbor_example.bin');
neighbor.save(fout);
fout.close();
// create a new Nearest Neighbor Anomaly model by loading the model
var fin = fs.openRead('neighbor_example.bin');
var neighbor2 = new analytics.NearestNeighborAD(fin);
});
});
var h = new ht.IntFltMap();
// Adding two key/dat pairs
h.put(5, 10.5);
h.put(15, 20.2);
// Getting data
h.hasKey(5); // returns true
h.get(15); // returns 20.2
h.key(1); // returns 15
h.dat(1); // returns 20.2
h.length; // returns 2
// Saving and loading:
var fs = require('qminer').fs;
fout = fs.openWrite('map.dat'); // open write stream
h.save(fout).close(); // save and close write stream
var h2 = new ht.IntFltMap(); // new empty table
var fin = fs.openRead('map.dat'); // open read stream
h2.load(fin); // load
});
});