Examples

import * as ds from '@tangent/ds';

const group1 = [5.1, 4.9, 4.7, 4.6, 5.0];
const group2 = [7.0, 6.4, 6.9, 6.5, 6.3];

const result = ds.stats.ttest(group1, group2);
console.log(`p-value: ${result.pValue}`);

const glm = new ds.stats.GLM({ family: 'gaussian' });
glm.fit({
  X: ['height', 'weight'],
  y: 'blood_pressure',
  data: healthData
});

console.log(glm.summary());

const knn = new ds.ml.KNNClassifier({ k: 5 });
knn.fit({
  data: trainData,
  X: ['feature1', 'feature2', 'feature3'],
  y: 'species'
});

const predictions = knn.predict({ data: testData, X: features });

const scores = ds.ml.validation.crossValidate(
  (Xtr, ytr) => new ds.ml.KNNClassifier({ k: 5 }).fit(Xtr, ytr),
  (model, Xte, yte) => ds.ml.metrics.accuracy(yte, model.predict(Xte)),
  { data: myData, X: features, y: 'target' },
  { k: 5, shuffle: true }
);

console.log(`Mean accuracy: ${scores.mean()}`);

import * as Plot from '@observablehq/plot';

const pca = new ds.mva.PCA({ center: true, scale: true });
pca.fit({ data: myData, X: features });

// Visualize
const biplot = ds.plot.ordiplot(pca.model);
document.body.appendChild(biplot.show(Plot));

const lda = new ds.mva.LDA();
lda.fit({
  data: myData,
  X: features,
  y: 'group'
});

const biplot = ds.plot.ordiplot(lda.model, {
  colorBy: myData.map(d => d.group)
});

const kmeans = new ds.ml.KMeans({ k: 3, random_state: 42 });
kmeans.fit({
  data: myData,
  columns: features,
  standardize: true
});

console.log(`Cluster labels: ${kmeans.labels}`);

const hca = new ds.ml.HCA({ linkage: 'ward' });
hca.fit({ data: myData, X: features });

// Plot dendrogram
const dendrogram = ds.plot.plotHCA(hca.model);