我试图在浏览器中运行javascript示例项目。我从https://github.com/stevenmiller888/mind获取了代码，并且我试图打开index.js。我尝试做他们在README文件中提到的内容，但我无法从二人组中打开它。请帮我解决一下这个。我是新手。
//这些是给出的步骤 $ npm install node-mind
您可以在浏览器中使用Mind，方法是使用Duo或Browserify。或者您可以直接使用预构建的根index.js文件，这将在窗口对象上显示Mind。
我也安装了node-moind和duo
// index.js - ＆gt;这是我需要运行的代码
!function(e){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=e();else if("function"==typeof define&&define.amd)define([],e);else{var f;"undefined"!=typeof window?f=window:"undefined"!=typeof global?f=global:"undefined"!=typeof self&&(f=self),f.Mind=e()}}(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
/**
* Dependencies.
*/
var sigmoidPrime = require('sigmoid-prime');
var Emitter = require('emitter-component');
var htanPrime = require('htan-prime');
var Matrix = require('node-matrix');
var sigmoid = require('sigmoid');
var sample = require('samples');
var htan = require('htan');
/**
* References.
*/
var scalar = Matrix.multiplyScalar;
var dot = Matrix.multiplyElements;
var multiply = Matrix.multiply;
var subtract = Matrix.subtract;
var add = Matrix.add;
/**
* Export `Mind`.
*/
module.exports = Mind;
/**
* Initialize a new `Mind`.
*
* @param {Object} opts
* @return {Object} this
* @api public
*/
function Mind(opts) {
if (!(this instanceof Mind)) return new Mind(opts);
opts = opts || {};
opts.activator === 'sigmoid'
? (this.activate = sigmoid, this.activatePrime = sigmoidPrime)
: (this.activate = htan, this.activatePrime = htanPrime);
// hyperparameters
this.learningRate = opts.learningRate || 0.7;
this.iterations = opts.iterations || 10000;
this.hiddenLayers = opts.hiddenLayers || 1;
this.hiddenUnits = opts.hiddenUnits || 3;
}
/**
* Mixin.
*/
Emitter(Mind.prototype);
/**
* Learn.
*
* 1. Normalize examples
* 2. Setup weights
* 3. Forward propagate to generate a prediction
* 4. Back propagate to adjust weights
* 5. Repeat (3) and (4) `this.iterations` times
*
* These five steps enable our network to learn the relationship
* between inputs and outputs.
*
* @param {Array} examples
* @return {Object} this
* @api public
*/
Mind.prototype.learn = function(examples) {
examples = normalize(examples);
this.setup(examples);
for (var i = 0; i < this.iterations; i++) {
var results = this.forward(examples);
var errors = this.back(examples, results);
this.emit('data', i, errors, results);
}
return this;
};
/**
* Setup the weights.
*
* @param {Object} examples
* @api private
*/
Mind.prototype.setup = function(examples) {
this.weights = [];
// input > hidden
this.weights.push(
Matrix({
rows: examples.input[0].length,
columns: this.hiddenUnits,
values: sample
})
);
// hidden > hidden
for (var i = 1; i < this.hiddenLayers; i++) {
this.weights.push(
Matrix({
rows: this.hiddenUnits,
columns: this.hiddenUnits,
values: sample
})
);
}
// hidden > output
this.weights.push(
Matrix({
rows: this.hiddenUnits,
columns: examples.output[0].length,
values: sample
})
);
};
/**
* Forward propagate.
*
* @param {Object} examples
* @return {Array} results
* @api private
*/
Mind.prototype.forward = function(examples) {
var activate = this.activate;
var weights = this.weights;
var results = [];
// sum the weight and input
function sum(w, i) {
var res = {};
res.sum = multiply(w, i);
res.result = res.sum.transform(activate);
return res;
};
// input > hidden
results.push(
sum(weights[0], examples.input)
);
// hidden > hidden
for (var i = 1; i < this.hiddenLayers; i++) {
results.push(
sum(weights[i], results[i - 1].result)
);
}
// hidden > output
results.push(
sum(weights[weights.length - 1], results[results.length - 1].result)
);
return results;
};
/**
* Back propagate.
*
* @param {Object} outputMatrix
* @api private
*/
Mind.prototype.back = function(examples, results) {
var activatePrime = this.activatePrime;
var hiddenLayers = this.hiddenLayers;
var learningRate = this.learningRate;
var weights = this.weights;
// output > hidden
var error = subtract(examples.output, results[results.length - 1].result);
var delta = dot(results[results.length - 1].sum.transform(activatePrime), error);
var changes = scalar(multiply(delta, results[0].result.transpose()), learningRate);
weights[weights.length - 1] = add(weights[weights.length - 1], changes);
// hidden > hidden
for (var i = 1; i < hiddenLayers; i++) {
delta = dot(multiply(weights[weights.length - i].transpose(), delta), results[results.length - (i + 1)].sum.transform(activatePrime));
changes = scalar(multiply(delta, results[results.length - (i + 1)].result.transpose()), learningRate);
weights[weights.length - (i + 1)] = add(weights[weights.length - (i + 1)], changes);
}
// hidden > input
delta = dot(multiply(weights[1].transpose(), delta), results[0].sum.transform(activatePrime));
changes = scalar(multiply(delta, examples.input.transpose()), learningRate);
weights[0] = add(weights[0], changes);
return error;
};
/**
* Predict.
*
* @param {Array} input
* @api public
*/
Mind.prototype.predict = function(input) {
var results = this.forward({ input: Matrix([input]) });
return results[results.length - 1].result[0];
};
/**
* Upload weights.
*
* @param {Object} weights
* @return {Object} this
* @api public
*/
Mind.prototype.upload = function(weights) {
this.weights = weights;
return this;
};
/**
* Download weights.
*
* @return {Object} weights
* @api public
*/
Mind.prototype.download = function() {
return this.weights;
};
/**
* Normalize the data.
*
* @param {Array} data
* @return {Object} ret
*/
function normalize(data) {
var ret = { input: [], output: [] };
for (var i = 0; i < data.length; i++) {
var datum = data[i];
ret.output.push(datum.output);
ret.input.push(datum.input);
}
ret.output = Matrix(ret.output);
ret.input = Matrix(ret.input);
return ret;
}
},{"emitter-component":2,"htan":4,"htan-prime":3,"node-matrix":5,"samples":6,"sigmoid":8,"sigmoid-prime":7}],2:[function(require,module,exports){
/**
* Expose `Emitter`.
*/
module.exports = Emitter;
/**
* Initialize a new `Emitter`.
*
* @api public
*/
function Emitter(obj) {
if (obj) return mixin(obj);
};
/**
* Mixin the emitter properties.
*
* @param {Object} obj
* @return {Object}
* @api private
*/
function mixin(obj) {
for (var key in Emitter.prototype) {
obj[key] = Emitter.prototype[key];
}
return obj;
}
/**
* Listen on the given `event` with `fn`.
*
* @param {String} event
* @param {Function} fn
* @return {Emitter}
* @api public
*/
Emitter.prototype.on =
Emitter.prototype.addEventListener = function(event, fn){
this._callbacks = this._callbacks || {};
(this._callbacks[event] = this._callbacks[event] || [])
.push(fn);
return this;
};
/**
* Adds an `event` listener that will be invoked a single
* time then automatically removed.
*
* @param {String} event
* @param {Function} fn
* @return {Emitter}
* @api public
*/
Emitter.prototype.once = function(event, fn){
var self = this;
this._callbacks = this._callbacks || {};
function on() {
self.off(event, on);
fn.apply(this, arguments);
}
on.fn = fn;
this.on(event, on);
return this;
};
/**
* Remove the given callback for `event` or all
* registered callbacks.
*
* @param {String} event
* @param {Function} fn
* @return {Emitter}
* @api public
*/
Emitter.prototype.off =
Emitter.prototype.removeListener =
Emitter.prototype.removeAllListeners =
Emitter.prototype.removeEventListener = function(event, fn){
this._callbacks = this._callbacks || {};
// all
if (0 == arguments.length) {
this._callbacks = {};
return this;
}
// specific event
var callbacks = this._callbacks[event];
if (!callbacks) return this;
// remove all handlers
if (1 == arguments.length) {
delete this._callbacks[event];
return this;
}
// remove specific handler
var cb;
for (var i = 0; i < callbacks.length; i++) {
cb = callbacks[i];
if (cb === fn || cb.fn === fn) {
callbacks.splice(i, 1);
break;
}
}
return this;
};
/**
* Emit `event` with the given args.
*
* @param {String} event
* @param {Mixed} ...
* @return {Emitter}
*/
Emitter.prototype.emit = function(event){
this._callbacks = this._callbacks || {};
var args = [].slice.call(arguments, 1)
, callbacks = this._callbacks[event];
if (callbacks) {
callbacks = callbacks.slice(0);
for (var i = 0, len = callbacks.length; i < len; ++i) {
callbacks[i].apply(this, args);
}
}
return this;
};
/**
* Return array of callbacks for `event`.
*
* @param {String} event
* @return {Array}
* @api public
*/
Emitter.prototype.listeners = function(event){
this._callbacks = this._callbacks || {};
return this._callbacks[event] || [];
};
/**
* Check if this emitter has `event` handlers.
*
* @param {String} event
* @return {Boolean}
* @api public
*/
Emitter.prototype.hasListeners = function(event){
return !! this.listeners(event).length;
};
},{}],3:[function(require,module,exports){
/**
* Expose `htanPrime`.
*/
module.exports = htanPrime;
/**
* Derivative of the hyperbolic tangent function.
*
* @param {Number} z
*/
function htanPrime(z) {
return 1 - Math.pow((Math.exp(2 * z) - 1) / (Math.exp(2 * z) + 1), 2);
}
},{}],4:[function(require,module,exports){
/**
* Expose `htan`.
*/
module.exports = htan;
/**
* Hyperbolic tangent function.
*
* - Useful for inputs between -1 and 1
*/
function htan(z) {
return (Math.exp(2 * z) - 1) / (Math.exp(2 * z) + 1);
}
},{}],5:[function(require,module,exports){
/**
* Expose `Matrix`.
*/
module.exports = Matrix;
/**
* Matrix.
*
* @param {Object|Array} opts
* @return {Object} this
*/
function Matrix(opts) {
if (!(this instanceof Matrix)) return new Matrix(opts);
if (Array.isArray(opts)) { // Passing in values
this.numRows = opts.length;
this.numCols = opts[0].length;
for (var i = 0; i < this.numRows; i++) {
this[i] = [];
for (var j = 0; j < this.numCols; j++) {
this[i][j] = opts[i][j];
}
}
} else if (typeof opts === 'object') { // Passing in dimensions
this.numRows = opts.rows;
this.numCols = opts.columns;
for (var i = 0; i < this.numRows; i++) {
this[i] = [];
for (var j = 0; j < this.numCols; j++) {
if (typeof opts.values === 'function') {
this[i][j] = opts.values();
} else if (typeof opts.values === 'number') {
this[i][j] = opts.values;
} else {
this[i][j] = 0;
}
}
}
} else {
throw new Error('You must supply an object or an array');
}
this.dimensions = [this.numRows, this.numCols];
}
/**
* Add.
*
* @param {Matrix} m1
* @param {Matrix} m2
* @return {Matrix} result
*/
Matrix.add = function(m1, m2) {
// Number of rows and columns in first must equal number of rows and columns in second
if (m1.numRows !== m2.numRows || m1.numCols !== m2.numCols) {
throw new Error('You can only add matrices with equal dimensions');
}
var result = new Matrix({ rows: m1.numRows, columns: m1.numCols });
for (var i = 0; i < m1.numRows; i++) {
for (var j = 0; j < m1.numCols; j++) {
result[i][j] = m1[i][j] + m2[i][j];
}
}
return result;
};
/**
* Subtract.
*
* @param {Matrix} m1
* @param {Matrix} m2
* @return {Matrix} result
*/
Matrix.subtract = function(m1, m2) {
// Number of rows and number of columns in first must equal number of rows and number of columns in second
if (m1.numRows !== m2.numRows || m1.numCols !== m2.numCols) {
throw new Error('You can only subtract matrices with equal dimensions');
}
var result = new Matrix({ rows: m1.numRows, columns: m1.numCols });
for (var i = 0; i < m1.numRows; i++) {
for (var j = 0; j < m1.numCols; j++) {
result[i][j] = m1[i][j] - m2[i][j];
}
}
return result;
};
/**
* Matrix multiplication.
*
* @param {Matrix} m1
* @param {Matrix} m2
* @return {Matrix} result
*/
Matrix.multiply = function(m1, m2) {
var result = Matrix({ rows: m2.numRows, columns: m1.numCols });
for (var i = 0; i < m2.numRows; i++) {
result[i] = [];
for (var j = 0; j < m1.numCols; j++) {
var sum = 0;
for (var k = 0; k < m1.numRows; k++) {
sum += m1[k][j] * m2[i][k];
}
result[i][j] = sum;
}
}
return result;
};
/**
* Scalar multiplication.
*
* @param {Matrix} m1
* @param {Number} num
* @return {Matrix} result
*/
Matrix.multiplyScalar = function(m1, num) {
var result = Matrix({ rows: m1.numRows, columns: m1.numCols });
for (var i = 0; i < m1.numRows; i++) {
for (var j = 0; j < m1.numCols; j++) {
result[i][j] = m1[i][j] * num;
}
}
return result;
};
/**
* Element-wise multiplcation.
*
* @param {Matrix} m1
* @param {Matrix} m2
* @return {Matrix} result
*/
Matrix.multiplyElements = function(m1, m2) {
var result = Matrix({ rows: m1.numRows, columns: m1.numCols })
for (var i = 0; i < m1.numRows; i++) {
result[i] = [];
for (var j = 0; j < m1[i].length; j++) {
result[i][j] = m1[i][j] * m2[i][j];
}
}
return result;
};
/**
* Compute the tranpose.
*
* @return {Matrix} result
*/
Matrix.prototype.transpose = function() {
var result = Matrix({ rows: this.numCols, columns: this.numRows });
for (var i = 0; i < this.numCols; i++) {
result[i] = [];
for (var j = 0; j < this.numRows; j++) {
result[i][j] = this[j][i];
}
}
return result;
};
/**
* Call a function on each element in the matrix.
*
* @param {Function} fn
* @return {Matrix} result
*/
Matrix.prototype.transform = function(fn) {
var result = Matrix({ rows: this.numRows, columns: this.numCols });
for (var i = 0; i < result.numRows; i++) {
for (var j = 0; j < result.numCols; j++) {
result[i][j] = fn(this[i][j]);
}
}
return result;
};
},{}],6:[function(require,module,exports){
/**
* Expose `sample`.
*/
module.exports = sample;
/**
* Generate a random sample from the Guassian distribution.
*
* - Uses the Box–Muller transform: https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
*/
function sample() {
return Math.sqrt(-2 * Math.log(Math.random())) * Math.cos(2 * Math.PI * Math.random());
}
},{}],7:[function(require,module,exports){
/**
* Expose `sigmoidPrime`.
*/
module.exports = sigmoidPrime;
/**
* Derivative of the sigmoid function.
*
* - Used to calculate the deltas in neural networks.
*
* @param {Number} z
*/
function sigmoidPrime(z) {
return Math.exp(-z) / Math.pow(1 + Math.exp(-z), 2);
}
},{}],8:[function(require,module,exports){
/**
* Expose `sigmoid`.
*/
module.exports = sigmoid;
/**
* sigmoid.
*
* - Non-linear, continuous, and differentiable logistic function.
*
* @param {Number} z
*/
function sigmoid(z) {
return 1 / (1 + Math.exp(-z));
}
},{}]},{},[1])(1)
});
答案 0 :(得分：0)
您需要编写自己的HTML / JS文件，您可以在浏览器中运行该文件，然后将头脑用作函数库。 index.js本身不在浏览器中运行。
＆＃34;或者您可以直接使用预建的根index.js文件，这将在窗口对象上显示Mind。＆＃34;
所以你可以通过JS文件中的window对象访问它。