@kobra-dev/js-regression - npm Package Compare versions

Comparing version 1.0.11 to 1.0.12

dist/index.d.ts

@@ -24,2 +24,7 @@ export declare class LogisticRegression {
     cost(X: any, Y: any, theta: any): number;
+    save(): {
+        theta: any[];
+        dim: any;
+    };
+    loadAndPredict(json: any, X: any): number | any[];
 }
@@ -26,0 +31,0 @@ export declare class MultiClassLogistic {

dist/js-regression.cjs.development.js

@@ -160,2 +160,15 @@ 'use strict';
 
+  _proto.save = function save() {
+    return {
+      theta: this.theta,
+      dim: this.dim
+    };
+  };
+
+  _proto.loadAndPredict = function loadAndPredict(json, X) {
+    this.theta = json.theta;
+    this.dim = json.dim;
+    return this.transform(X);
+  };
+
   return LogisticRegression;
@@ -162,0 +175,0 @@ }();

dist/js-regression.cjs.production.min.js

@@ -1,2 +0,2 @@
-"use strict";Object.defineProperty(exports,"__esModule",{value:!0});var t=function(){function t(t){this.theta=[],(t=t||{}).alpha||(t.alpha=.001),t.iterations||(t.iterations=100),t.lambda||(t.lambda=0),this.alpha=t.alpha,this.lambda=t.lambda,this.iterations=t.iterations}var s=t.prototype;return s.fit=function(t){this.dim=t[0].length;for(var s=t.length,a=[],h=[],i=0;i<s;++i){var r=t[i],e=[],o=r[r.length-1];e.push(1);for(var n=0;n<r.length-1;++n)e.push(r[n]);a.push(e),h.push(o)}this.theta=[];for(var l=0;l<this.dim;++l)this.theta.push(0);for(var f=0;f<this.iterations;++f){var u=this.grad(a,h,this.theta);for(l=0;l<this.dim;++l)this.theta[l]=this.theta[l]-this.alpha*u[l]}return this.threshold=this.computeThreshold(a,h),{theta:this.theta,threshold:this.threshold,cost:this.cost(a,h,this.theta),config:{alpha:this.alpha,lambda:this.lambda,iterations:this.iterations}}},s.computeThreshold=function(t,s){for(var a=1,h=t.length,i=0;i<h;++i){var r=this.transform(t[i]);1==s[i]&&a>r&&(a=r)}return a},s.grad=function(t,s,a){for(var h=t.length,i=[],r=0;r<this.dim;++r){for(var e=0,o=0;o<h;++o){var n=t[o];e+=((this.h(n,a)-s[o])*n[r]+this.lambda*a[r])/h}i.push(e)}return i},s.h=function(t,s){for(var a=0,h=0;h<this.dim;++h)a+=s[h]*t[h];return 1/(1+Math.exp(-a))},s.transform=function(t){if(t[0].length){for(var s=[],a=0;a<t.length;++a){var h=this.transform(t[a]);s.push(h)}return s}var i=[];i.push(1);for(var r=0;r<t.length;++r)i.push(t[r]);return this.h(i,this.theta)},s.cost=function(t,s,a){for(var h=t.length,i=0,r=0;r<h;++r){var e=s[r],o=t[r];i+=-(e*Math.log(this.h(o,a))+(1-e)*Math.log(1-this.h(o,a)))/h}for(var n=0;n<this.dim;++n)i+=this.lambda*a[n]*a[n]/(2*h);return i},t}(),s=function(){function s(t){(t=t||{}).alpha||(t.alpha=.001),t.iterations||(t.iterations=100),t.lambda||(t.lambda=0),this.alpha=t.alpha,this.lambda=t.lambda,this.iterations=t.iterations}var a=s.prototype;return a.fit=function(s,a){this.dim=s[0].length;var h=s.length;if(!a){a=[];for(var i=0;i<h;++i){for(var r=!1,e=s[i][this.dim-1],o=0;o<a.length;++o)if(e==a[o]){r=!0;break}r||a.push(e)}}this.classes=a,this.logistics={};for(var n={},l=0;l<this.classes.length;++l){var f=this.classes[l];this.logistics[f]=new t({alpha:this.alpha,lambda:this.lambda,iterations:this.iterations});var u=[];for(i=0;i<h;++i){var p=[];for(o=0;o<this.dim-1;++o)p.push(s[i][o]);p.push(s[i][this.dim-1]==f?1:0),u.push(p)}n[f]=this.logistics[f].fit(u)}return n},a.transform=function(t){if(t[0].length){for(var s=[],a=0;a<t.length;++a){var h=this.transform(t[a]);s.push(h)}return s}for(var i=0,r="",e=0;e<this.classes.length;++e){var o=this.classes[e],n=this.logistics[o].transform(t);i<n&&(i=n,r=o)}return r},s}();exports.LogisticRegression=t,exports.MultiClassLogistic=s;
+"use strict";Object.defineProperty(exports,"__esModule",{value:!0});var t=function(){function t(t){this.theta=[],(t=t||{}).alpha||(t.alpha=.001),t.iterations||(t.iterations=100),t.lambda||(t.lambda=0),this.alpha=t.alpha,this.lambda=t.lambda,this.iterations=t.iterations}var i=t.prototype;return i.fit=function(t){this.dim=t[0].length;for(var i=t.length,s=[],h=[],a=0;a<i;++a){var r=t[a],e=[],n=r[r.length-1];e.push(1);for(var o=0;o<r.length-1;++o)e.push(r[o]);s.push(e),h.push(n)}this.theta=[];for(var l=0;l<this.dim;++l)this.theta.push(0);for(var f=0;f<this.iterations;++f){var u=this.grad(s,h,this.theta);for(l=0;l<this.dim;++l)this.theta[l]=this.theta[l]-this.alpha*u[l]}return this.threshold=this.computeThreshold(s,h),{theta:this.theta,threshold:this.threshold,cost:this.cost(s,h,this.theta),config:{alpha:this.alpha,lambda:this.lambda,iterations:this.iterations}}},i.computeThreshold=function(t,i){for(var s=1,h=t.length,a=0;a<h;++a){var r=this.transform(t[a]);1==i[a]&&s>r&&(s=r)}return s},i.grad=function(t,i,s){for(var h=t.length,a=[],r=0;r<this.dim;++r){for(var e=0,n=0;n<h;++n){var o=t[n];e+=((this.h(o,s)-i[n])*o[r]+this.lambda*s[r])/h}a.push(e)}return a},i.h=function(t,i){for(var s=0,h=0;h<this.dim;++h)s+=i[h]*t[h];return 1/(1+Math.exp(-s))},i.transform=function(t){if(t[0].length){for(var i=[],s=0;s<t.length;++s){var h=this.transform(t[s]);i.push(h)}return i}var a=[];a.push(1);for(var r=0;r<t.length;++r)a.push(t[r]);return this.h(a,this.theta)},i.cost=function(t,i,s){for(var h=t.length,a=0,r=0;r<h;++r){var e=i[r],n=t[r];a+=-(e*Math.log(this.h(n,s))+(1-e)*Math.log(1-this.h(n,s)))/h}for(var o=0;o<this.dim;++o)a+=this.lambda*s[o]*s[o]/(2*h);return a},i.save=function(){return{theta:this.theta,dim:this.dim}},i.loadAndPredict=function(t,i){return this.theta=t.theta,this.dim=t.dim,this.transform(i)},t}(),i=function(){function i(t){(t=t||{}).alpha||(t.alpha=.001),t.iterations||(t.iterations=100),t.lambda||(t.lambda=0),this.alpha=t.alpha,this.lambda=t.lambda,this.iterations=t.iterations}var s=i.prototype;return s.fit=function(i,s){this.dim=i[0].length;var h=i.length;if(!s){s=[];for(var a=0;a<h;++a){for(var r=!1,e=i[a][this.dim-1],n=0;n<s.length;++n)if(e==s[n]){r=!0;break}r||s.push(e)}}this.classes=s,this.logistics={};for(var o={},l=0;l<this.classes.length;++l){var f=this.classes[l];this.logistics[f]=new t({alpha:this.alpha,lambda:this.lambda,iterations:this.iterations});var u=[];for(a=0;a<h;++a){var d=[];for(n=0;n<this.dim-1;++n)d.push(i[a][n]);d.push(i[a][this.dim-1]==f?1:0),u.push(d)}o[f]=this.logistics[f].fit(u)}return o},s.transform=function(t){if(t[0].length){for(var i=[],s=0;s<t.length;++s){var h=this.transform(t[s]);i.push(h)}return i}for(var a=0,r="",e=0;e<this.classes.length;++e){var n=this.classes[e],o=this.logistics[n].transform(t);a<o&&(a=o,r=n)}return r},i}();exports.LogisticRegression=t,exports.MultiClassLogistic=i;
 //# sourceMappingURL=js-regression.cjs.production.min.js.map

dist/js-regression.esm.js

@@ -156,2 +156,15 @@ var LogisticRegression = /*#__PURE__*/function () {
 
+  _proto.save = function save() {
+    return {
+      theta: this.theta,
+      dim: this.dim
+    };
+  };
+
+  _proto.loadAndPredict = function loadAndPredict(json, X) {
+    this.theta = json.theta;
+    this.dim = json.dim;
+    return this.transform(X);
+  };
+
   return LogisticRegression;
@@ -158,0 +171,0 @@ }();

package.json

 {
-  "version": "1.0.11",
-  "description": "Package implements linear regression and logistic regression",
-  "license": "MIT",
-  "main": "dist/index.js",
-  "typings": "dist/index.d.ts",
-  "repository": {
-    "type": "git",
-    "url": "git+https://github.com/kobra-dev/js-regression.git"
-  },
-  "keywords": [
-    "linear regression",
-    "logistic regression",
-    "polynomial regression",
-    "multiple linear regression",
-    "classification",
-    "binary classification",
-    "multi-class classification",
-    "prediction",
-    "linear models",
-    "least squares",
-    "gradient descent",
-    "regression"
-  ],
-  "files": [
-    "dist",
-    "src"
-  ],
-  "engines": {
-    "node": ">=10"
-  },
-  "scripts": {
-    "start": "tsdx watch",
-    "build": "tsdx build",
-    "test": "tsdx test",
-    "lint": "tsdx lint",
-    "prepare": "tsdx build",
-    "size": "size-limit",
-    "analyze": "size-limit --why"
-  },
-  "peerDependencies": {},
-  "husky": {
-    "hooks": {
-      "pre-commit": "tsdx lint"
+    "version": "1.0.12",
+    "description": "Package implements linear regression and logistic regression",
+    "license": "MIT",
+    "main": "dist/index.js",
+    "typings": "dist/index.d.ts",
+    "repository": {
+        "type": "git",
+        "url": "git+https://github.com/kobra-dev/js-regression.git"
+    },
+    "keywords": [
+        "linear regression",
+        "logistic regression",
+        "polynomial regression",
+        "multiple linear regression",
+        "classification",
+        "binary classification",
+        "multi-class classification",
+        "prediction",
+        "linear models",
+        "least squares",
+        "gradient descent",
+        "regression"
+    ],
+    "files": [
+        "dist",
+        "src"
+    ],
+    "engines": {
+        "node": ">=10"
+    },
+    "scripts": {
+        "start": "tsdx watch",
+        "build": "tsdx build",
+        "test": "tsdx test",
+        "lint": "tsdx lint",
+        "prepare": "tsdx build",
+        "size": "size-limit",
+        "analyze": "size-limit --why"
+    },
+    "peerDependencies": {},
+    "husky": {
+        "hooks": {
+            "pre-commit": "tsdx lint"
+        }
+    },
+    "prettier": {
+        "printWidth": 80,
+        "semi": true,
+        "tabWidth": 4
+    },
+    "name": "@kobra-dev/js-regression",
+    "author": "Kobra, Xianshun Chen",
+    "contributors": [
+        "Xianshun Chen <[e-mail]>",
+        "The Kobra Authors <[e-mail]>"
+    ],
+    "module": "dist/js-regression.esm.js",
+    "devDependencies": {
+        "@size-limit/preset-small-lib": "^4.12.0",
+        "husky": "^6.0.0",
+        "js-datasets-iris": "^1.0.4",
+        "size-limit": "^4.12.0",
+        "tsdx": "^0.14.1",
+        "tslib": "^2.3.0",
+        "typescript": "^4.3.4"
     }
-  },
-  "prettier": {
-    "printWidth": 80,
-    "semi": true,
-    "tabWidth": 4
-  },
-  "name": "@kobra-dev/js-regression",
-  "author": "Kobra, Xianshun Chen",
-  "contributors": [
-    "Xianshun Chen <[e-mail]>",
-    "The Kobra Authors <[e-mail]>"
-  ],
-  "module": "dist/js-regression.esm.js",
-  "devDependencies": {
-    "@size-limit/preset-small-lib": "^4.12.0",
-    "husky": "^6.0.0",
-    "js-datasets-iris": "^1.0.4",
-    "size-limit": "^4.12.0",
-    "tsdx": "^0.14.1",
-    "tslib": "^2.3.0",
-    "typescript": "^4.3.4"
-  }
 }

src/index.ts

@@ -64,4 +64,4 @@ export class LogisticRegression {
                 lambda: this.lambda,
-                iterations: this.iterations
-            }
+                iterations: this.iterations,
+            },
         };
@@ -146,2 +146,16 @@ }
     }
+
+    save() {
+        return {
+            theta: this.theta,
+            dim: this.dim,
+        };
+    }
+
+    loadAndPredict(json: any, X: any) {
+        this.theta = json.theta;
+        this.dim = json.dim;
+
+        return this.transform(X);
+    }
 }
@@ -203,3 +217,3 @@
                 lambda: this.lambda,
-                iterations: this.iterations
+                iterations: this.iterations,
             });
@@ -206,0 +220,0 @@ var data_c = [];

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Improved metrics

Total package byte prevSize

72619

increased by4.14%

Lines of code

720

increased by5.73%

Worsened metrics

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1

increased byInfinity%

No dependency changes