new Sigmoid([arg])

Sigmoid function (y = 1/[1 + exp[-A*x + B]]) fitted on decision function to mimic.

Example

// import modules
la = require('qminer').la;
analytics = require('qminer').analytics;
// create a new model
var sigmoid = new analytics.Sigmoid();
// generate a random predictions
var x = new la.Vector([0.5, 2.3, -0.1, 0.5, -7.3, 1.2]);
// generate a random labels
var y = new la.Vector([1, 1, -1, 1, -1, -1]);
// fit model
sigmoid.fit(x, y);
// get predictions
var pred1 = sigmoid.predict(1.2);
var pred2 = sigmoid.predict(-1.2);

Parameter

Name Type Optional Description

arg

module:fs.FIn

Yes

Construction arguments.

Methods

decisionFunction(x) → (number or module:la.Vector)

Returns the expected response for the provided feature vector.

Example

// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// create the predicted values and the binary labels
var X = new la.Vector([-3, -2, -1, 1, 2, 3]);
var y = new la.Vector([-1, -1, -1, 1, 1, 1]);
// fit the model
s.fit(X, y);
// predict the probability of the value 0 on this model
// returns 0.5
var prediction = s.decisionFunction(0.5);

Parameter

Name Type Optional Description

x

(number or module:la.Vector)

 

Prediction score.

Returns

(number or module:la.Vector) 
1. If x is a number, returns a normalized prediction score,
2. if x is a module:la.Vector, returns a vector of normalized prediction scores.

fit(x, y) → module:analytics.Sigmoid

Fits a column matrix of feature vectors X onto the response variable y.

Example

// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// create the predicted values and the binary labels
var X = new la.Vector([-3, -2, -1, 1, 2, 3]);
var y = new la.Vector([-1, -1, -1, 1, 1, 1]);
// fit the model
// changes the internal A and B values of the model 
s.fit(X, y);

Parameters

Name Type Optional Description

x

module:la.Vector

 

Predicted values (e.g. using module:analytics.SVR).

y

module:la.Vector

 

Actual binary labels: 1 or -1.

Returns

module:analytics.Sigmoid Self. The model has been created.

getModel() → Object

Gets the model.

Example

// import analytics module
var analytics = require('qminer').analytics;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// get the model parameters
// returns a Json object { A: 0, B: 0 }
var model = s.getModel();
Returns

Object The object sigModel containing the properties:
sigModel.A - First value of the Sigmoid model,
sigModel.B - Second value of the Sigmoid model.

getParams() → Object

Get the parameters. It doesn't do anything, it's only for consistency for constructing pipeline.

Example

// import analytics module
var analytics = require('qminer').analytics;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// get the parameters
// returns an empty object
var param = s.getParams();
Returns

Object An empty object.

predict(x) → (number or module:la.Vector)

Returns the expected response for the provided feature vector.

Example

// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// create the predicted values and the binary labels
var X = new la.Vector([-3, -2, -1, 1, 2, 3]);
var y = new la.Vector([-1, -1, -1, 1, 1, 1]);
// fit the model
s.fit(X, y);
// predict the probability of the value 0 on this model
// returns 0.5
var prediction = s.predict(0.5);

Parameter

Name Type Optional Description

x

(number or module:la.Vector)

 

Prediction score.

Returns

(number or module:la.Vector) 
1. If x is a number, returns a normalized prediction score,
2. if x is a module:la.Vector, returns a vector of normalized prediction scores.

save(fout) → module:fs.FOut

Saves the model into the output stream.

Example

// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// create the predicted values and the binary labels
var X = new la.Vector([-3, -2, -1, 1, 2, 3]);
var y = new la.Vector([-1, -1, -1, 1, 1, 1]);
// fit the model
s.fit(X, y);
// create an output stream object and save the model
var fout = fs.openWrite('sigmoid_example.bin');
s.save(fout);
fout.close();
// create a new Sigmoid model by loading the model
var fin = fs.openRead('sigmoid_example.bin');
var s2 = new analytics.Sigmoid(fin);

Parameter

Name Type Optional Description

fout

module:fs.FOut

 

Output stream.

Returns

module:fs.FOut The output stream fout.

setParams(arg) → module:analytics.Sigmoid

Sets the parameters. It doesn't do anything, it's only for consistency for constructing pipeline.

Example

// import analytics module
var analytics = require('qminer').analytics;
// create the Sigmoid model
var s = new analytics.Sigmoid();
// set the parameters 
// doesn't change the model
s.setParams({});

Parameter

Name Type Optional Description

arg

Object

 

Json object.

Returns

module:analytics.Sigmoid Self. Nothing changes.