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Commit 4bf0c8b

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‎src/index.js‎

Lines changed: 43 additions & 45 deletions
Original file line numberDiff line numberDiff line change
@@ -188,10 +188,6 @@ const trainLogisticRegression = async (featureCount, trainDs, validDs) => {
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};
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const trainComplexModel = async (featureCount, trainDs, validDs) => {
191-
// const arr = await ds.take(10).toArray();
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// console.log(arr[0].xs.arraySync());
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const model = tf.sequential();
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model.add(
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tf.layers.dense({
@@ -233,50 +229,37 @@ const trainComplexModel = async (featureCount, trainDs, validDs) => {
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const run = async () => {
235231
const data = await prepareData();
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// console.log(data[0]);
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// renderOutcomes(data);
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// renderHistogram("insulin-cont", data, "Insulin", {
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// title: "Insulin levels",
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// xLabel: "Insulin 2-hour serum, mu U/ml"
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// });
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// renderHistogram("glucose-cont", data, "Glucose", {
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// title: "Glucose concentration",
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// xLabel: "Plasma glucose concentration (2 hour after glucose tolerance test)"
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// });
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// renderHistogram("age-cont", data, "Age", {
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// title: "Age",
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// xLabel: "age (years)"
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// });
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// renderScatter("glucose-age-cont", data, ["Glucose", "Age"], {
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// title: "Glucose vs Age",
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// xLabel: "Glucose",
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// yLabel: "Age"
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// });
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// renderScatter("skin-bmi-cont", data, ["SkinThickness", "BMI"], {
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// title: "Skin thickness vs BMI",
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// xLabel: "Skin thickness",
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// yLabel: "BMI"
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// });
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// const [xTrain, xTest, yTrain, yTest] = toTensors(
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// data,
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// ["Glucose", "Age", "BMI"],
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// 0.1
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// );
272232

273-
// const features = ["Glucose"];
233+
renderOutcomes(data);
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235+
renderHistogram("insulin-cont", data, "Insulin", {
236+
title: "Insulin levels",
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xLabel: "Insulin 2-hour serum, mu U/ml"
238+
});
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renderHistogram("glucose-cont", data, "Glucose", {
241+
title: "Glucose concentration",
242+
xLabel: "Plasma glucose concentration (2 hour after glucose tolerance test)"
243+
});
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renderHistogram("age-cont", data, "Age", {
246+
title: "Age",
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xLabel: "age (years)"
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});
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275-
// const [trainDs, validDs] = createDataSets(data, features, 0.1, 16);
250+
renderScatter("glucose-age-cont", data, ["Glucose", "Age"], {
251+
title: "Glucose vs Age",
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xLabel: "Glucose",
253+
yLabel: "Age"
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});
276255

277-
// trainLogisticRegression(features.length, trainDs, validDs);
256+
renderScatter("skin-bmi-cont", data, ["SkinThickness", "BMI"], {
257+
title: "Skin thickness vs BMI",
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xLabel: "Skin thickness",
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yLabel: "BMI"
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});
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279-
const features = ["Glucose","Age","Insulin","BloodPressure"];
262+
const features = ["Glucose"];
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281264
const [trainDs, validDs, xTest, yTest] = createDataSets(
282265
data,
@@ -285,7 +268,22 @@ const run = async () => {
285268
16
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);
287270

288-
const model = await trainComplexModel(features.length, trainDs, validDs);
271+
const model = await trainLogisticRegression(
272+
features.length,
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trainDs,
274+
validDs
275+
);
276+
277+
// const features = ["Glucose", "Age", "Insulin", "BloodPressure"];
278+
279+
// const [trainDs, validDs, xTest, yTest] = createDataSets(
280+
// data,
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// features,
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// 0.1,
283+
// 16
284+
// );
285+
286+
// const model = await trainComplexModel(features.length, trainDs, validDs);
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290288
const preds = model.predict(xTest).argMax(-1);
291289
const labels = yTest.argMax(-1);

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