Change return value to Array

Author: ishii-norimi

js/view/ica.js

@@ -9,8 +9,8 @@ var dispICA = function (elm, platform) {
 				const dim = platform.dimension
 				const model = new ICA()
 				model.fit(tx)
-				let y = model.predict(tx, dim)
-				pred_cb(y.toArray())
+				const y = model.predict(tx, dim)
+				pred_cb(y)
 			})
 		})
 }

js/view/isomap.js

@@ -5,8 +5,8 @@ var dispIsomap = function (elm, platform) {
 		const neighbors = +elm.select('[name=neighbors]').property('value')
 		platform.fit((tx, ty, pred_cb) => {
 			const dim = platform.dimension
-			let y = Isomap(tx, dim, neighbors)
-			pred_cb(y.toArray())
+			const y = Isomap(tx, dim, neighbors)
+			pred_cb(y)
 		})
 	}
 

js/view/laplacian_eigenmaps.js

@@ -43,7 +43,7 @@ var dispLE = function (elm, platform) {
 				const dim = platform.dimension
 				const model = new LaplacianEigenmaps(method, k, sigma)
 				const pred = model.predict(tx, dim)
-				pred_cb(pred.toArray())
+				pred_cb(pred)
 			})
 		})
 }

js/view/lda.js

@@ -30,7 +30,7 @@ var dispLDA = function (elm, platform) {
 			} else {
 				const dim = platform.dimension
 				let y = LinearDiscriminantAnalysis(tx, ty, dim)
-				pred_cb(y.toArray())
+				pred_cb(y)
 			}
 			cb && cb()
 		})

js/view/lle.js

@@ -5,8 +5,8 @@ var dispLLE = function (elm, platform) {
 		platform.fit((tx, ty, pred_cb) => {
 			const neighbor = +elm.select('[name=neighbor_size]').property('value')
 			const dim = platform.dimension
-			let y = LLE(tx, neighbor, dim)
-			pred_cb(y.toArray())
+			const y = LLE(tx, neighbor, dim)
+			pred_cb(y)
 		})
 	}
 

js/view/lsa.js

@@ -7,8 +7,8 @@ var dispLSA = function (elm, platform) {
 		.on('click', () => {
 			platform.fit((tx, ty, pred_cb) => {
 				const dim = platform.dimension
-				let y = LSA(tx, dim)
-				pred_cb(y.toArray())
+				const y = LSA(tx, dim)
+				pred_cb(y)
 			})
 		})
 }

js/view/mds.js

@@ -4,8 +4,8 @@ var dispMDS = function (elm, platform) {
 	const fitModel = cb => {
 		platform.fit((tx, ty, pred_cb) => {
 			const dim = platform.dimension
-			let y = MDS(tx, dim)
-			pred_cb(y.toArray())
+			const y = MDS(tx, dim)
+			pred_cb(y)
 		})
 	}
 

js/view/nmf.js

@@ -1,5 +1,7 @@
 import NMF from '../../lib/model/nmf.js'
 
+import { Matrix } from '../../lib/util/math.js'
+
 var dispNMF = function (elm, platform) {
 	let model = null
 
@@ -12,7 +14,7 @@ var dispNMF = function (elm, platform) {
 					model.init(tx, k)
 				}
 				model.fit()
-				const pred = model.predict()
+				const pred = Matrix.fromArray(model.predict())
 				pred_cb(pred.argmax(1).value.map(v => v + 1))
 			} else {
 				if (!model) {
@@ -22,7 +24,7 @@ var dispNMF = function (elm, platform) {
 				}
 				model.fit()
 				const pred = model.predict()
-				pred_cb(pred.toArray())
+				pred_cb(pred)
 			}
 			cb && cb()
 		})

js/view/pca.js

@@ -10,8 +10,8 @@ var dispPCA = function (elm, platform) {
 				const dim = platform.dimension
 				const model = new PCA(kernel)
 				model.fit(tx)
-				let y = model.predict(tx, dim)
-				pred_cb(y.toArray())
+				const y = model.predict(tx, dim)
+				pred_cb(y)
 			} else {
 				const model = new AnomalyPCA()
 				model.fit(tx)

js/view/principal_curve.js

@@ -12,8 +12,8 @@ var dispPC = function (elm, platform) {
 			platform.fit((tx, ty, pred_cb) => {
 				const dim = platform.dimension
 				model.fit(tx)
-				let y = model.predict(tx, dim)
-				pred_cb(y.toArray())
+				const y = model.predict(tx, dim)
+				pred_cb(y)
 				cb && cb()
 			})
 		})

js/view/random_projection.js

@@ -5,8 +5,8 @@ var dispRandomProjection = function (elm, platform) {
 		const init = elm.select('[name=init]').property('value')
 		platform.fit((tx, ty, pred_cb) => {
 			const dim = platform.dimension
-			let y = RandomProjection(tx, dim, init)
-			pred_cb(y.toArray())
+			const y = RandomProjection(tx, dim, init)
+			pred_cb(y)
 		})
 	}
 

js/view/sammon.js

@@ -9,7 +9,7 @@ var dispSammon = function (elm, platform) {
 				model = new Sammon(tx, dim)
 			}
 			const pred = model.fit()
-			pred_cb(pred.toArray())
+			pred_cb(pred)
 			cb && cb()
 		})
 	}

js/view/tsne.js

@@ -9,8 +9,8 @@ var dispTSNE = function (elm, platform) {
 			return
 		}
 		platform.fit((tx, ty, pred_cb) => {
-			let y = model.fit()
-			pred_cb(y.toArray())
+			const y = model.fit()
+			pred_cb(y)
 
 			cb && cb()
 		})

lib/model/ica.js

@@ -20,7 +20,7 @@ export default class ICA {
 		x = x.copySub(x.mean(0))
 		const pca = new PCA()
 		pca.fit(x)
-		const z = pca.predict(x)
+		const z = Matrix.fromArray(pca.predict(x))
 		const eps = 1.0e-12
 		const r = []
 
@@ -68,6 +68,6 @@ export default class ICA {
 		if (rd > 0 && rd < w.cols) {
 			w = w.resize(w.rows, rd)
 		}
-		return x.dot(w)
+		return x.dot(w).toArray()
 	}
 }

lib/model/laplacian_eigenmaps.js

@@ -58,6 +58,6 @@ export class LaplacianEigenmaps {
 
 		this._ev = L.eigenVectors()
 		this._ev.flip(1)
-		return this._ev.sliceCol(1, rd + 1)
+		return this._ev.sliceCol(1, rd + 1).toArray()
 	}
 }

lib/model/lda.js

@@ -174,5 +174,5 @@ export const LinearDiscriminantAnalysis = function (x, t, rd = 0) {
 	if (rd > 0 && rd < ev.cols) {
 		ev = ev.resize(ev.rows, rd)
 	}
-	return x.dot(ev)
+	return x.dot(ev).toArray()
 }

lib/model/lle.js

@@ -56,7 +56,7 @@ const LLE = function (x, K = 1, rd = 0) {
 	const mtm = m.tDot(m)
 	let ev = mtm.eigenVectors()
 	ev.flip(1)
-	return ev.sliceCol(1, rd + 1)
+	return ev.sliceCol(1, rd + 1).toArray()
 }
 
 export default LLE

lib/model/lsa.js

@@ -8,6 +8,7 @@ const LSA = function (x, rd = 0) {
 		.sliceCol(0, rd)
 		.dot(Matrix.diag(s.slice(0, rd)))
 		.dot(v.slice(0, 0, rd, rd).t)
+		.toArray()
 }
 
 export default LSA

lib/model/mds.js

@@ -40,5 +40,5 @@ export const MDS = function (x, rd = 1, dmat = false) {
 			evec.multAt(i, k, Math.sqrt(evalue[k]))
 		}
 	}
-	return evec.sliceCol(0, rd)
+	return evec.sliceCol(0, rd).toArray()
 }

lib/model/nmf.js

@@ -44,6 +44,6 @@ export default class NMF {
 	}
 
 	predict() {
-		return this._H.t
+		return this._H.t.toArray()
 	}
 }

lib/model/pca.js

@@ -56,7 +56,7 @@ export class PCA {
 			w = w.resize(w.rows, rd)
 		}
 		x = this._gram(x)
-		return x.dot(w)
+		return x.dot(w).toArray()
 	}
 }
 

lib/model/pcr.js

@@ -12,7 +12,7 @@ export default class PCR {
 		x = Matrix.fromArray(x)
 		y = Matrix.fromArray(y)
 		this._pca.fit(x)
-		let xh = this._pca.predict(x, this._rd)
+		let xh = Matrix.fromArray(this._pca.predict(x, this._rd))
 		xh = xh.resize(xh.rows, xh.cols + 1, 1)
 		const xtx = xh.tDot(xh)
 
@@ -21,7 +21,7 @@ export default class PCR {
 
 	predict(x) {
 		x = Matrix.fromArray(x)
-		let xh = this._pca.predict(x, this._rd)
+		let xh = Matrix.fromArray(this._pca.predict(x, this._rd))
 		xh = x.resize(xh.rows, xh.cols + 1, 1)
 		return xh.dot(this._w).toArray()
 	}

lib/model/principal_curve.js

@@ -64,6 +64,6 @@ export default class PrincipalCurve {
 	}
 
 	predict(x, rd = 0) {
-		return this._l
+		return this._l.toArray()
 	}
 }

lib/model/random_projection.js

@@ -22,7 +22,7 @@ const RandomProjection = function (x, rd = 0, init = 'uniform') {
 	} else {
 		w = Matrix.random(x.cols, d, -1, 1)
 	}
-	return x.dot(w)
+	return x.dot(w).toArray()
 }
 
 export default RandomProjection

lib/model/sammon.js

@@ -49,6 +49,6 @@ export default class Sammon {
 				this._y.subAt(i, j, (this._alpha * de.at(0, j)) / Math.abs(dde.at(0, j)))
 			}
 		}
-		return this._y
+		return this._y.toArray()
 	}
 }

lib/model/tsne.js

@@ -115,7 +115,7 @@ export class SNE {
 		}
 		this._epoch += 1
 
-		return new Matrix(n, d, this._y)
+		return new Matrix(n, d, this._y).toArray()
 	}
 }
 
@@ -233,6 +233,6 @@ export class tSNE {
 		}
 		this._epoch += 1
 
-		return new Matrix(n, d, this._y)
+		return new Matrix(n, d, this._y).toArray()
 	}
 }

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@ai-on-browser/data-analysis-models",
-  "version": "0.0.1",
+  "version": "0.0.2",
   "description": "Data analysis models written completely in Javascript",
   "type": "module",
   "main": "index.js",