How to use the ml.default.Regression function in ml

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github repetere / modelscript / src / ml.js View on Github external
import { default as MachineLearning, } from 'ml';
import { RandomForestRegression, RandomForestClassifier, } from 'ml-random-forest';
import { default as LogisticRegression, } from 'ml-logistic-regression';
import { DecisionTreeRegression, DecisionTreeClassifier, } from 'ml-cart';
import { GaussianNB, } from 'ml-naivebayes';
import { default as MultivariateLinearRegression, } from 'ml-regression-multivariate-linear';
import { default as PCA, } from 'ml-pca';
import { ReinforcedLearningBase, UpperConfidenceBound, ThompsonSampling, } from './ReinforcedLearning';

MachineLearning.Regression = Object.assign({},
  MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
  MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
  MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
  MachineLearning.RL, {
    ReinforcedLearningBase,
    UpperConfidenceBound,
    ThompsonSampling,
  });
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;
github repetere / modelscript / src / ml.js View on Github external
import { default as MachineLearning, } from 'ml';
import { RandomForestRegression, RandomForestClassifier, } from 'ml-random-forest';
import { default as LogisticRegression, } from 'ml-logistic-regression';
import { DecisionTreeRegression, DecisionTreeClassifier, } from 'ml-cart';
import { GaussianNB, } from 'ml-naivebayes';
import { default as MultivariateLinearRegression, } from 'ml-regression-multivariate-linear';
import { default as PCA, } from 'ml-pca';
import { ReinforcedLearningBase, UpperConfidenceBound, ThompsonSampling, } from './ReinforcedLearning';

MachineLearning.Regression = Object.assign({},
  MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
  MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
  MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
  MachineLearning.RL, {
    ReinforcedLearningBase,
    UpperConfidenceBound,
    ThompsonSampling,
  });
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;
github repetere / modelscript / src / ml.js View on Github external
MachineLearning.Regression = Object.assign({},
  MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
  MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
  MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
  MachineLearning.RL, {
    ReinforcedLearningBase,
    UpperConfidenceBound,
    ThompsonSampling,
  });
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;

MachineLearning.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;

MachineLearning.Stat.PCA = PCA;

/**
 * @namespace
 * @see {@link https://github.com/mljs/ml} 
 */
export const ml = MachineLearning;
github repetere / modelscript / src / ml.js View on Github external
MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
  MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
  MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
  MachineLearning.RL, {
    ReinforcedLearningBase,
    UpperConfidenceBound,
    ThompsonSampling,
  });
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;

MachineLearning.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;

MachineLearning.Stat.PCA = PCA;

/**
 * @namespace
 * @see {@link https://github.com/mljs/ml} 
 */
export const ml = MachineLearning;
github repetere / modelscript / src / ml.js View on Github external
MachineLearning.Regression = Object.assign({},
  MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
  MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
  MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
  MachineLearning.RL, {
    ReinforcedLearningBase,
    UpperConfidenceBound,
    ThompsonSampling,
  });
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;

MachineLearning.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;

MachineLearning.Stat.PCA = PCA;

/**
 * @namespace
 * @see {@link https://github.com/mljs/ml} 
 */
export const ml = MachineLearning;