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@ -8,6 +8,8 @@ github.com/shaunrd0/klips/
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├── blockchain # Blockchain related project templates and examples
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├── cpp # C++ programs, datastructures, and other examples
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├── figlet # Figlet fonts I like :)
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├── javascript # Javascript projects and examples
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├── python # Python scripts or tools I've made
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├── README.md
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└── scripts # Bash scripts
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```
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@ -0,0 +1,9 @@
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# Javascript
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This directory contains Javascript projects and examples that I've made.
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```
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github.com/shaunrd0/klips/javascript
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├── webgl # Examples of using WebGL within JS
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└── README.md
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```
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@ -0,0 +1,165 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>01_simple-shape</title>
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<style>
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.test {
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background-color: gray;
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}
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canvas {
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background-color: gray;
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width: 100%;
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height: 100%;
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display: block;
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}
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</style>
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</head>
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<body>
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<!-- Simple example of shader source code (WebGL Fundamentals) -->
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<script id="vertex-simple-shader" type="x-shader/x-vertex">
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attribute vec2 a_position;
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uniform vec2 u_resolution;
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void main() {
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// Convert pixel coordinates to a float randing from 0.0 -> 1.0
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vec2 zeroToOne = a_position / u_resolution;
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// Convert from 0->1 to 0->2
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vec2 zeroToTwo = zeroToOne * 2.0;
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// Convert from 0->2 to -1.0->1.0
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vec2 clipSpace = zeroToTwo - 1.0;
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gl_Position = vec4(clipSpace * vec2(1, -1), 0, 1);
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}
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</script>
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<script id="fragment-simple-shader" type="x-shader/x-vertex">
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precision mediump float;
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uniform vec4 u_color;
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void main() {
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gl_FragColor = u_color;
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}
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</script>
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<!-- HTML document -->
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<p class="test">This is test HTML with some CSS. Logging with JS...<br>The following block is an OpenGL canvas</p>
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<canvas id="canvas"></canvas>
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<!-- WebGL -->
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<script>
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function main() {
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//
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// Boilerplate OpenGL helper functions
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function createShader(gl, type, source) {
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let shader = gl.createShader(type);
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gl.shaderSource(shader, source);
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gl.compileShader(shader);
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let success = gl.getShaderParameter(shader, gl.COMPILE_STATUS);
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if (success) {
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return shader;
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}
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console.log(gl.getShaderInfoLog(shader));
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gl.deleteShader(shader);
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}
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function createProgram(gl, vertexShader, fragmentShader) {
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// Add check to automatically compile shaders if Strings are provided
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// + Strings should be equal to HTML script id attribute value for vertex and fragment shaders
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if (typeof vertexShader == 'string') {
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let vertexShaderSource = document.querySelector(vertexShader).text;
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vertexShader = createShader(gl, gl.VERTEX_SHADER, vertexShaderSource);
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}
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if (typeof fragmentShader == 'string') {
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let fragmentShaderSource = document.querySelector(fragmentShader).text;
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fragmentShader = createShader(gl, gl.FRAGMENT_SHADER, fragmentShaderSource);
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}
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let program = gl.createProgram();
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gl.attachShader(program, vertexShader);
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gl.attachShader(program, fragmentShader);
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gl.linkProgram(program);
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let success = gl.getProgramParameter(program, gl.LINK_STATUS);
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if (success) {
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return program;
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}
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console.log(gl.getProgramInfoLog(program));
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gl.deleteProgram(program);
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}
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let canvas = document.querySelector("#canvas");
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let gl = canvas.getContext('webgl');
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if (!gl) {
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console.log("ERROR: Unable to get OpenGL context");
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return;
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}
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else {
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console.log("Created OpenGL context on HTML canvas")
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}
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// Creating shader program and compiling shader source code
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let program = createProgram(gl, "#vertex-simple-shader", "#fragment-simple-shader");
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// look up where the vertex data needs to go.
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let positionAttributeLocation = gl.getAttribLocation(program, "a_position");
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// look up uniform locations
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let resolutionUniformLocation = gl.getUniformLocation(program, "u_resolution");
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let colorUniformLocation = gl.getUniformLocation(program, "u_color");
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// Create a buffer to put three 2d clip space points in
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let positionBuffer = gl.createBuffer();
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// Bind it to ARRAY_BUFFER (think of it as ARRAY_BUFFER = positionBuffer)
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gl.bindBuffer(gl.ARRAY_BUFFER, positionBuffer);
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// Canvas setup
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// Resize canvas to match client size
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const width = canvas.clientWidth;
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const height = canvas.clientHeight;
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canvas.width = width;
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canvas.height = height;
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gl.viewport(0, 0, gl.canvas.width, gl.canvas.height);
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// Clear the canvas
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gl.clearColor(0, 0, 0, 0);
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gl.clear(gl.COLOR_BUFFER_BIT);
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// Tell OpenGL to use our shader program
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gl.useProgram(program);
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// Enable attribute; Bind gl.ARRAY_BUFFER for use with this attribute
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gl.enableVertexAttribArray(positionAttributeLocation);
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gl.bindBuffer(gl.ARRAY_BUFFER, positionBuffer);
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// Tell the attribute how to get data out of positionBuffer (ARRAY_BUFFER)
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let size = 2; // 2 components per iteration (X and Y value for each vertex position)
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let type = gl.FLOAT; // Each X and Y value is a 32bit float
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let normalize = false; // don't normalize the data
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let stride = 0; // 0 = move forward size * sizeof(type) each iteration to get the next position
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let offset = 0; // start at the beginning of the buffer
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gl.vertexAttribPointer(positionAttributeLocation, size, type, normalize, stride, offset);
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// Initialize geometry data for a 2D triangle with 3 vertices
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let triangle = new Float32Array([
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350, 100,
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500, 300,
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200, 300,
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]);
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// Write geometry data to positions array buffer
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gl.bufferData(gl.ARRAY_BUFFER, triangle, gl.STATIC_DRAW);
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// Set a random color
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gl.uniform4f(colorUniformLocation, Math.random(), Math.random(), Math.random(), 1);
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// set the resolution
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gl.uniform2f(resolutionUniformLocation, gl.canvas.width, gl.canvas.height);
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// Draw the triangle with 0 offset and 3 total vertices
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gl.drawArrays(gl.TRIANGLES, 0, 3);
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}
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main();
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</script>
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</body>
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</html>
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@ -0,0 +1,9 @@
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# Javascript
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This directory contains examples of using WebGL within JavaScript.
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```
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github.com/shaunrd0/klips/javascript/webgl
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├── 01_simple-shape # Drawing a simple solid color triangle with WebGL
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└── README.md
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```
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@ -32,20 +32,6 @@ optional arguments:
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Optionally provide file for data to be read from. Each point must be on it's own line with format x,y
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```
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Running linear regression program
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```bash
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python3.9 linear-regression.py --file ./input.txt --silent
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Finding fitting line plot for given data [(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)]
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points_avg: (5.117647058823529, 5.235294117647059)
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variance: (241.76470588235296, 193.05882352941177)
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sigma: (3.887196176892422, 3.4736402333270258)
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covariance: 0.8455882352941174
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correlation: 0.0626235432924427
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Our line Y = BX + A must pass through the point (5.117647058823529, 5.235294117647059)
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Y = (0.05596107055961069)X + 4.9489051094890515
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For X = 4.5, Y is predicted to be 5.200729927007299
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```
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By default, the following linear regression is calculated and displayed
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```bash
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python3.9 linear-regression.py
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(0, 2, 1, 0, 2) has probability: 0.001395
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```
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By default, a random Hidden Markov Model and visualization will be generated
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By default, a random Hidden Markov Model and visualization will be generated and the sequence `[A, B, D, C]` will be used.
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```bash
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python3.9 markov-model.py
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Neural network implementation using Python CLI to dynamically generate a resizable network
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and then run a given number of learning cycles on the provided data set.
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As an example, the IRIS dataset is used to classify flower types using petal measurements.
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Input layer perceptron count can be adjusted with `INPUTS` positional parameter
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Hidden layer perceptron count can be adjusted with `PERCEPTRONS` positional parameter
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Output layer perceptron count can be adjusted with `OUTPUTS` positional parameter
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Hidden layers can be added or removed using`--hidden-layers` option setting
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Input layer perceptron count can be adjusted with `INPUTS` positional parameter.
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Hidden layer perceptron count can be adjusted with `PERCEPTRONS` positional parameter.
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Output layer perceptron count can be adjusted with `OUTPUTS` positional parameter.
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Hidden layers can be added or removed using`--hidden-layers` option setting.
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Node bias can be initialized randomly or with provided data.
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Perceptron edge weight bias can be initialized randomly or with provided data.
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Threshold for perceptron fire can be initialized randomly or with provided data.
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If the length of possible label classifications does not match the number of output nodes requested, a warning will show.
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In both cases, the program corrects the node count to match the input data / labels, and not the requested node count.
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The total number of output labels provided must match the total number of the number of input sequences.
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The total number of output labels provided must match the total number of input sequences.
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Running NN program uses IRIS data set by default.
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Warnings will be shown if input and output node count is changed without providing new input.
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Loading…
Reference in New Issue