/* dataset: https://archive.ics.uci.edu/ml/datasets/seeds in short, you have a bag of seeds, contains 3 different kinds of seeds. the program sorts them. i did this in a supervised neural network training but this could also be done, and would probably make more sense for the context where we dont know the labels already, with unsupervised learning(eg k means clusters). the training can be run longer for slightly more precision. */ const rawData=`15.26 14.84 0.871 5.763 3.312 2.221 5.22 1 14.88 14.57 0.8811 5.554 3.333 1.018 4.956 1 14.29 14.09 0.905 5.291 3.337 2.699 4.825 1 13.84 13.94 0.8955 5.324 3.379 2.259 4.805 1 16.14 14.99 0.9034 5.658 3.562 1.355 5.175 1 14.38 14.21 0.8951 5.386 3.312 2.462 4.956 1 14.69 14.49 0.8799 5.563 3.259 3.586 5.219 1 14.11 14.1 0.8911 5.42 3.302 2.7 5 1 16.63 15.46 0.8747 6.053 3.465 2.04 5.877 1 16.44 15.25 0.888 5.884 3.505 1.969 5.533 1 15.26 14.85 0.8696 5.714 3.242 4.543 5.314 1 14.03 14.16 0.8796 5.438 3.201 1.717 5.001 1 13.89 14.02 0.888 5.439 3.199 3.986 4.738 1 13.78 14.06 0.8759 5.479 3.156 3.136 4.872 1 13.74 14.05 0.8744 5.482 3.114 2.932 4.825 1 14.59 14.28 0.8993 5.351 3.333 4.185 4.781 1 13.99 13.83 0.9183 5.119 3.383 5.234 4.781 1 15.69 14.75 0.9058 5.527 3.514 1.599 5.046 1 14.7 14.21 0.9153 5.205 3.466 1.767 4.649 1 12.72 13.57 0.8686 5.226 3.049 4.102 4.914 1 14.16 14.4 0.8584 5.658 3.129 3.072 5.176 1 14.11 14.26 0.8722 5.52 3.168 2.688 5.219 1 15.88 14.9 0.8988 5.618 3.507 0.7651 5.091 1 12.08 13.23 0.8664 5.099 2.936 1.415 4.961 1 15.01 14.76 0.8657 5.789 3.245 1.791 5.001 1 16.19 15.16 0.8849 5.833 3.421 0.903 5.307 1 13.02 13.76 0.8641 5.395 3.026 3.373 4.825 1 12.74 13.67 0.8564 5.395 2.956 2.504 4.869 1 14.11 14.18 0.882 5.541 3.221 2.754 5.038 1 13.45 14.02 0.8604 5.516 3.065 3.531 5.097 1 13.16 13.82 0.8662 5.454 2.975 0.8551 5.056 1 15.49 14.94 0.8724 5.757 3.371 3.412 5.228 1 14.09 14.41 0.8529 5.717 3.186 3.92 5.299 1 13.94 14.17 0.8728 5.585 3.15 2.124 5.012 1 15.05 14.68 0.8779 5.712 3.328 2.129 5.36 1 16.12 15 0.9 5.709 3.485 2.27 5.443 1 16.2 15.27 0.8734 5.826 3.464 2.823 5.527 1 17.08 15.38 0.9079 5.832 3.683 2.956 5.484 1 14.8 14.52 0.8823 5.656 3.288 3.112 5.309 1 14.28 14.17 0.8944 5.397 3.298 6.685 5.001 1 13.54 13.85 0.8871 5.348 3.156 2.587 5.178 1 13.5 13.85 0.8852 5.351 3.158 2.249 5.176 1 13.16 13.55 0.9009 5.138 3.201 2.461 4.783 1 15.5 14.86 0.882 5.877 3.396 4.711 5.528 1 15.11 14.54 0.8986 5.579 3.462 3.128 5.18 1 13.8 14.04 0.8794 5.376 3.155 1.56 4.961 1 15.36 14.76 0.8861 5.701 3.393 1.367 5.132 1 14.99 14.56 0.8883 5.57 3.377 2.958 5.175 1 14.79 14.52 0.8819 5.545 3.291 2.704 5.111 1 14.86 14.67 0.8676 5.678 3.258 2.129 5.351 1 14.43 14.4 0.8751 5.585 3.272 3.975 5.144 1 15.78 14.91 0.8923 5.674 3.434 5.593 5.136 1 14.49 14.61 0.8538 5.715 3.113 4.116 5.396 1 14.33 14.28 0.8831 5.504 3.199 3.328 5.224 1 14.52 14.6 0.8557 5.741 3.113 1.481 5.487 1 15.03 14.77 0.8658 5.702 3.212 1.933 5.439 1 14.46 14.35 0.8818 5.388 3.377 2.802 5.044 1 14.92 14.43 0.9006 5.384 3.412 1.142 5.088 1 15.38 14.77 0.8857 5.662 3.419 1.999 5.222 1 12.11 13.47 0.8392 5.159 3.032 1.502 4.519 1 11.42 12.86 0.8683 5.008 2.85 2.7 4.607 1 11.23 12.63 0.884 4.902 2.879 2.269 4.703 1 12.36 13.19 0.8923 5.076 3.042 3.22 4.605 1 13.22 13.84 0.868 5.395 3.07 4.157 5.088 1 12.78 13.57 0.8716 5.262 3.026 1.176 4.782 1 12.88 13.5 0.8879 5.139 3.119 2.352 4.607 1 14.34 14.37 0.8726 5.63 3.19 1.313 5.15 1 14.01 14.29 0.8625 5.609 3.158 2.217 5.132 1 14.37 14.39 0.8726 5.569 3.153 1.464 5.3 1 12.73 13.75 0.8458 5.412 2.882 3.533 5.067 1 17.63 15.98 0.8673 6.191 3.561 4.076 6.06 2 16.84 15.67 0.8623 5.998 3.484 4.675 5.877 2 17.26 15.73 0.8763 5.978 3.594 4.539 5.791 2 19.11 16.26 0.9081 6.154 3.93 2.936 6.079 2 16.82 15.51 0.8786 6.017 3.486 4.004 5.841 2 16.77 15.62 0.8638 5.927 3.438 4.92 5.795 2 17.32 15.91 0.8599 6.064 3.403 3.824 5.922 2 20.71 17.23 0.8763 6.579 3.814 4.451 6.451 2 18.94 16.49 0.875 6.445 3.639 5.064 6.362 2 17.12 15.55 0.8892 5.85 3.566 2.858 5.746 2 16.53 15.34 0.8823 5.875 3.467 5.532 5.88 2 18.72 16.19 0.8977 6.006 3.857 5.324 5.879 2 20.2 16.89 0.8894 6.285 3.864 5.173 6.187 2 19.57 16.74 0.8779 6.384 3.772 1.472 6.273 2 19.51 16.71 0.878 6.366 3.801 2.962 6.185 2 18.27 16.09 0.887 6.173 3.651 2.443 6.197 2 18.88 16.26 0.8969 6.084 3.764 1.649 6.109 2 18.98 16.66 0.859 6.549 3.67 3.691 6.498 2 21.18 17.21 0.8989 6.573 4.033 5.78 6.231 2 20.88 17.05 0.9031 6.45 4.032 5.016 6.321 2 20.1 16.99 0.8746 6.581 3.785 1.955 6.449 2 18.76 16.2 0.8984 6.172 3.796 3.12 6.053 2 18.81 16.29 0.8906 6.272 3.693 3.237 6.053 2 18.59 16.05 0.9066 6.037 3.86 6.001 5.877 2 18.36 16.52 0.8452 6.666 3.485 4.933 6.448 2 16.87 15.65 0.8648 6.139 3.463 3.696 5.967 2 19.31 16.59 0.8815 6.341 3.81 3.477 6.238 2 18.98 16.57 0.8687 6.449 3.552 2.144 6.453 2 18.17 16.26 0.8637 6.271 3.512 2.853 6.273 2 18.72 16.34 0.881 6.219 3.684 2.188 6.097 2 16.41 15.25 0.8866 5.718 3.525 4.217 5.618 2 17.99 15.86 0.8992 5.89 3.694 2.068 5.837 2 19.46 16.5 0.8985 6.113 3.892 4.308 6.009 2 19.18 16.63 0.8717 6.369 3.681 3.357 6.229 2 18.95 16.42 0.8829 6.248 3.755 3.368 6.148 2 18.83 16.29 0.8917 6.037 3.786 2.553 5.879 2 18.85 16.17 0.9056 6.152 3.806 2.843 6.2 2 17.63 15.86 0.88 6.033 3.573 3.747 5.929 2 19.94 16.92 0.8752 6.675 3.763 3.252 6.55 2 18.55 16.22 0.8865 6.153 3.674 1.738 5.894 2 18.45 16.12 0.8921 6.107 3.769 2.235 5.794 2 19.38 16.72 0.8716 6.303 3.791 3.678 5.965 2 19.13 16.31 0.9035 6.183 3.902 2.109 5.924 2 19.14 16.61 0.8722 6.259 3.737 6.682 6.053 2 20.97 17.25 0.8859 6.563 3.991 4.677 6.316 2 19.06 16.45 0.8854 6.416 3.719 2.248 6.163 2 18.96 16.2 0.9077 6.051 3.897 4.334 5.75 2 19.15 16.45 0.889 6.245 3.815 3.084 6.185 2 18.89 16.23 0.9008 6.227 3.769 3.639 5.966 2 20.03 16.9 0.8811 6.493 3.857 3.063 6.32 2 20.24 16.91 0.8897 6.315 3.962 5.901 6.188 2 18.14 16.12 0.8772 6.059 3.563 3.619 6.011 2 16.17 15.38 0.8588 5.762 3.387 4.286 5.703 2 18.43 15.97 0.9077 5.98 3.771 2.984 5.905 2 15.99 14.89 0.9064 5.363 3.582 3.336 5.144 2 18.75 16.18 0.8999 6.111 3.869 4.188 5.992 2 18.65 16.41 0.8698 6.285 3.594 4.391 6.102 2 17.98 15.85 0.8993 5.979 3.687 2.257 5.919 2 20.16 17.03 0.8735 6.513 3.773 1.91 6.185 2 17.55 15.66 0.8991 5.791 3.69 5.366 5.661 2 18.3 15.89 0.9108 5.979 3.755 2.837 5.962 2 18.94 16.32 0.8942 6.144 3.825 2.908 5.949 2 15.38 14.9 0.8706 5.884 3.268 4.462 5.795 2 16.16 15.33 0.8644 5.845 3.395 4.266 5.795 2 15.56 14.89 0.8823 5.776 3.408 4.972 5.847 2 15.38 14.66 0.899 5.477 3.465 3.6 5.439 2 17.36 15.76 0.8785 6.145 3.574 3.526 5.971 2 15.57 15.15 0.8527 5.92 3.231 2.64 5.879 2 15.6 15.11 0.858 5.832 3.286 2.725 5.752 2 16.23 15.18 0.885 5.872 3.472 3.769 5.922 2 13.07 13.92 0.848 5.472 2.994 5.304 5.395 3 13.32 13.94 0.8613 5.541 3.073 7.035 5.44 3 13.34 13.95 0.862 5.389 3.074 5.995 5.307 3 12.22 13.32 0.8652 5.224 2.967 5.469 5.221 3 11.82 13.4 0.8274 5.314 2.777 4.471 5.178 3 11.21 13.13 0.8167 5.279 2.687 6.169 5.275 3 11.43 13.13 0.8335 5.176 2.719 2.221 5.132 3 12.49 13.46 0.8658 5.267 2.967 4.421 5.002 3 12.7 13.71 0.8491 5.386 2.911 3.26 5.316 3 10.79 12.93 0.8107 5.317 2.648 5.462 5.194 3 11.83 13.23 0.8496 5.263 2.84 5.195 5.307 3 12.01 13.52 0.8249 5.405 2.776 6.992 5.27 3 12.26 13.6 0.8333 5.408 2.833 4.756 5.36 3 11.18 13.04 0.8266 5.22 2.693 3.332 5.001 3 11.36 13.05 0.8382 5.175 2.755 4.048 5.263 3 11.19 13.05 0.8253 5.25 2.675 5.813 5.219 3 11.34 12.87 0.8596 5.053 2.849 3.347 5.003 3 12.13 13.73 0.8081 5.394 2.745 4.825 5.22 3 11.75 13.52 0.8082 5.444 2.678 4.378 5.31 3 11.49 13.22 0.8263 5.304 2.695 5.388 5.31 3 12.54 13.67 0.8425 5.451 2.879 3.082 5.491 3 12.02 13.33 0.8503 5.35 2.81 4.271 5.308 3 12.05 13.41 0.8416 5.267 2.847 4.988 5.046 3 12.55 13.57 0.8558 5.333 2.968 4.419 5.176 3 11.14 12.79 0.8558 5.011 2.794 6.388 5.049 3 12.1 13.15 0.8793 5.105 2.941 2.201 5.056 3 12.44 13.59 0.8462 5.319 2.897 4.924 5.27 3 12.15 13.45 0.8443 5.417 2.837 3.638 5.338 3 11.35 13.12 0.8291 5.176 2.668 4.337 5.132 3 11.24 13 0.8359 5.09 2.715 3.521 5.088 3 11.02 13 0.8189 5.325 2.701 6.735 5.163 3 11.55 13.1 0.8455 5.167 2.845 6.715 4.956 3 11.27 12.97 0.8419 5.088 2.763 4.309 5 3 11.4 13.08 0.8375 5.136 2.763 5.588 5.089 3 10.83 12.96 0.8099 5.278 2.641 5.182 5.185 3 10.8 12.57 0.859 4.981 2.821 4.773 5.063 3 11.26 13.01 0.8355 5.186 2.71 5.335 5.092 3 10.74 12.73 0.8329 5.145 2.642 4.702 4.963 3 11.48 13.05 0.8473 5.18 2.758 5.876 5.002 3 12.21 13.47 0.8453 5.357 2.893 1.661 5.178 3 11.41 12.95 0.856 5.09 2.775 4.957 4.825 3 12.46 13.41 0.8706 5.236 3.017 4.987 5.147 3 12.19 13.36 0.8579 5.24 2.909 4.857 5.158 3 11.65 13.07 0.8575 5.108 2.85 5.209 5.135 3 12.89 13.77 0.8541 5.495 3.026 6.185 5.316 3 11.56 13.31 0.8198 5.363 2.683 4.062 5.182 3 11.81 13.45 0.8198 5.413 2.716 4.898 5.352 3 10.91 12.8 0.8372 5.088 2.675 4.179 4.956 3 11.23 12.82 0.8594 5.089 2.821 7.524 4.957 3 10.59 12.41 0.8648 4.899 2.787 4.975 4.794 3 10.93 12.8 0.839 5.046 2.717 5.398 5.045 3 11.27 12.86 0.8563 5.091 2.804 3.985 5.001 3 11.87 13.02 0.8795 5.132 2.953 3.597 5.132 3 10.82 12.83 0.8256 5.18 2.63 4.853 5.089 3 12.11 13.27 0.8639 5.236 2.975 4.132 5.012 3 12.8 13.47 0.886 5.16 3.126 4.873 4.914 3 12.79 13.53 0.8786 5.224 3.054 5.483 4.958 3 13.37 13.78 0.8849 5.32 3.128 4.67 5.091 3 12.62 13.67 0.8481 5.41 2.911 3.306 5.231 3 12.76 13.38 0.8964 5.073 3.155 2.828 4.83 3 12.38 13.44 0.8609 5.219 2.989 5.472 5.045 3 12.67 13.32 0.8977 4.984 3.135 2.3 4.745 3 11.18 12.72 0.868 5.009 2.81 4.051 4.828 3 12.7 13.41 0.8874 5.183 3.091 8.456 5 3 12.37 13.47 0.8567 5.204 2.96 3.919 5.001 3 12.19 13.2 0.8783 5.137 2.981 3.631 4.87 3 11.23 12.88 0.8511 5.14 2.795 4.325 5.003 3 13.2 13.66 0.8883 5.236 3.232 8.315 5.056 3 11.84 13.21 0.8521 5.175 2.836 3.598 5.044 3 12.3 13.34 0.8684 5.243 2.974 5.637 5.063 3`; const seedCode=[ [1,0,0], [0,1,0], [0,0,1] ]; let xs,ys; let testXs,testYs; let neuralNet; function parseData(){ let samples=rawData.split("\n"); shuffleArray(samples); xs=new Array(samples.length-84); ys=new Array(samples.length-84); testXs = new Array(84); testYs = new Array(84); for(let i=0;i 0; i--) { var j = Math.floor(Math.random() * (i + 1)); var temp = array[i]; array[i] = array[j]; array[j] = temp; } } function addPolynomials(varlist,degree){ polys=[]; for(let combo=0;combo<(degree)**varlist.length;combo++){ comboStr=combo.toString(degree).padStart(varlist.length,"0"); prod=1; for(let v=0;v{ h.history.loss.forEach((l)=>console.log("cost: "+l)); }); } function scale(xs){ let scaled=new Array(xs.length); for(let i=0;i=max?x:max; min=x<=min?x:min; } range=max-min; ranges.push(max-min); mins.push(min); for(let i=0;i{ return sample.map((feature,j)=>{ // if(j!=0){ return (testXs[i][j]-mins[j])/ranges[j];//}else{ // return 1; // } }) }); inputsTNSR=tf.tensor(xs); outputsTNSR=tf.tensor(ys); testInputsTNSR=tf.tensor(testXs); initNeuralNet(inputsTNSR,outputsTNSR) .then(()=>{ let preds= neuralNet.predict(testInputsTNSR).arraySync(); let accurates=0; preds.forEach((p,i)=>{ if(p.indexOf(Math.max(...p))==testYs[i].indexOf(1)) accurates++; }); console.log(`${accurates}/84 accuracy on test set. ${100*accurates/84}%`); let preds2= neuralNet.predict(inputsTNSR).arraySync(); accurates=0; preds2.forEach((p,i)=>{ if(p.indexOf(Math.max(...p))==ys[i].indexOf(1)){accurates++;}; }); console.log(`${accurates}/126 accuracy on training set. ${100*accurates/126}%`); }); }