linear_model.Lasso
Usage
import { Iris } from 'machinelearn/datasets';
import { Lasso } from 'machinelearn/linear_model';
(async function() {
const irisData = new Iris();
const {
data, // returns the iris data (X)
targets, // list of target values (y)
} = await irisData.load(); // loads the data internally
const reg = new Lasso({ degree: 2, l1: 1 });
reg.fit(data, target);
reg.predict([[5.1,3.5,1.4,0.2]]);
})();
Constructors
Properties
Methods
Constructors
constructor
⊕ Lasso(__namedParameters: `object`)
Defined in linear_model/coordinate_descent.ts:103
Parameters:
Param | Type | Default | Description |
---|---|---|---|
options.degree | number | null | |
options.epochs | number | 1000 | |
options.l1 | number | ||
options.learning_rate | number | 0.001 |
Returns: Lasso
Properties
▸ epochs
Defined in linear_model/stochastic_gradient.ts:27
▸ learningRate
Defined in linear_model/stochastic_gradient.ts:26
▸ loss
Defined in linear_model/stochastic_gradient.ts:28
▸ regFactor
Defined in linear_model/stochastic_gradient.ts:29
Methods
λ fit
Fit model with coordinate descent.
Defined in linear_model/coordinate_descent.ts:141
Parameters:
Param | Type | Default | Description |
---|---|---|---|
X | number[][] | null | A matrix of samples |
y | number[] | null | A vector of targets |
Returns:
void
λ fromJSON
Restore the model from a checkpoint
Defined in linear_model/stochastic_gradient.ts:151
Parameters:
Param | Type | Default | Description |
---|---|---|---|
options.epochs | number | 10000 | |
options.learning_rate | number | 0.0001 | |
options.random_state | number | null | |
options.weights | number[] | [] |
Returns:
void
λ predict
Predict using the linear model
Defined in linear_model/coordinate_descent.ts:151
Parameters:
Param | Type | Default | Description |
---|---|---|---|
X | number[][] | null | A matrix of test data |
Returns:
number[]
λ toJSON
Save the model's checkpoint
Defined in linear_model/stochastic_gradient.ts:118
Returns:
Param | Type | Description |
---|---|---|
epochs | number | model training epochs |
learning_rate | number | model learning rate |
random_state | number | Number used to set a static random state |
weights | number[] | Model training weights |