demo/perceptron: Support initializing more weights. (#4)

* demo/perceptron: Support initializing more weights.
* demo/deposit cost: Get slightly most accurate deposit cost.

demo/client/contracts/classification/Perceptron.sol

@@ -32,10 +32,36 @@ contract Perceptron is Classifier64 {
         }
     }
 
+    /**
+     * Initialize weights for the model.
+     * Made to be called just after the contract is created and never again.
+     * @param startIndex The index to start placing `_weights` into the model's weights.
+     * @param _weights The weights to set for the model.
+     */
+    function initializeWeights(uint64 startIndex, int80[] memory _weights) public onlyOwner {
+        for (uint64 i = 0; i < _weights.length; ++i) {
+            weights[startIndex + i] = _weights[i];
+        }
+    }
+
     function norm(int64[] memory /* data */) public pure returns (uint) {
         revert("Normalization is not required.");
     }
 
+    function predict(int64[] memory data) public view returns (uint64) {
+        int m = intercept;
+        for (uint i = 0; i < data.length; ++i) {
+            // `update` assumes this check is done.
+            require(data[i] >= 0, "Not all indices are >= 0.");
+            m = m.add(weights[uint64(data[i])]);
+        }
+        if (m <= 0) {
+            return 0;
+        } else {
+            return 1;
+        }
+    }
+
     function update(int64[] memory data, uint64 classification) public onlyOwner {
         uint64 prediction = predict(data);
         if (prediction != classification) {
@@ -59,35 +85,6 @@ contract Perceptron is Classifier64 {
         }
     }
 
-    /**
-     * Check if two arrays of training data are equal.
-     */
-    function isDataEqual(uint24[] memory d1, uint24[] memory d2) public pure returns (bool) {
-        if (d1.length != d2.length) {
-            return false;
-        }
-        for (uint i = 0; i < d1.length; ++i) {
-            if (d1[i] != d2[i]) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-    function predict(int64[] memory data) public view returns (uint64) {
-        int m = intercept;
-        for (uint i = 0; i < data.length; ++i) {
-            // `update` assumes this check is done.
-            require(data[i] >= 0, "Not all indices are >= 0.");
-            m = m.add(weights[uint64(data[i])]);
-        }
-        if (m <= 0) {
-            return 0;
-        } else {
-            return 1;
-        }
-    }
-
     /**
      * Evaluate a batch.
      *

demo/client/migrations/2_deploy_sentiment_classifier.js

@@ -7,7 +7,7 @@ const DataHandler64 = artifacts.require("./data/DataHandler64");
 const Classifier = artifacts.require("./classification/Perceptron");
 const Stakeable64 = artifacts.require("./incentive/Stakeable64");
 
-module.exports = function (deployer) {
+module.exports = async function (deployer) {
   // Information to persist to the DB.
   const modelInfo = {
     name: "IMDB Review Sentiment Classifier",
@@ -34,12 +34,9 @@ module.exports = function (deployer) {
   var data = fs.readFileSync('./src/ml-models/imdb-sentiment-model.json', 'utf8');
   var model = JSON.parse(data);
 
-  // Don't use all the words since it takes too long to load
-  // and we don't need them all just for simple testing.
-  var maxNumWords = 100;
-
-  // There are 18 decimal places.
-  var weights = convertData(model['coef'].slice(0, maxNumWords));
+  const weights = convertData(model['coef']);
+  const initNumWords = 250;
+  const numWordsPerUpdate = 250;
 
   console.log(`Deploying IMDB model with ${weights.length} weights.`);
   var intercept = web3.utils.toBN(model['intercept'] * toFloat);
@@ -55,8 +52,17 @@ module.exports = function (deployer) {
       costWeight
     ).then(incentiveMechanism => {
       console.log(`  Deployed incentive mechanism to ${incentiveMechanism.address}.`);
+      console.log(`Deploying classifier.`);
       return deployer.deploy(Classifier,
-        classifications, weights, intercept, learningRate).then(classifier => {
+        classifications, weights.slice(0, initNumWords), intercept, learningRate,
+        { gas: 7.9E6 }).then(async classifier => {
+          console.log(`  Deployed classifier to ${classifier.address}.`);
+          for (let i = initNumWords; i < weights.length; i += numWordsPerUpdate) {
+            await classifier.initializeWeights(i, weights.slice(i, i + numWordsPerUpdate),
+              { gas: 7.9E6 });
+            console.log(`  Added weights ${i + numWordsPerUpdate}`);
+          }
+
           console.log(`Deploying collaborative trainer contract.`);
           return deployer.deploy(CollaborativeTrainer64,
             dataHandler.address,

demo/client/src/components/model.js

@@ -286,8 +286,8 @@ class Model extends React.Component {
     return this.state.incentiveMechanism.methods.lastUpdateTimeS().call()
       .then(parseInt)
       .then(lastUpdateTimeS => {
-        var now = new Date().getTime() / 1000;
-        var divisor = now - lastUpdateTimeS;
+        const now = Math.floor(new Date().getTime() / 1000);
+        let divisor = now - lastUpdateTimeS;
         if (divisor === 0) {
           divisor = 1;
         } else {