apache-ignite

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// Licensed to the Apache Software Foundation (ASF) under one or more
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// contributor license agreements.  See the NOTICE file distributed with
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// The ASF licenses this file to You under the Apache License, Version 2.0
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// (the "License"); you may not use this file except in compliance with
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// the License.  You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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= k-NN Regression
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The Apache Ignite Machine Learning component provides two versions of the widely used k-NN (k-nearest neighbors) algorithm - one for classification tasks and the other for regression tasks.
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This documentation reviews k-NN as a solution for regression tasks.
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== Trainer and Model
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The k-NN regression algorithm is a non-parametric method whose input consists of the k-closest training examples in the feature space. Each training example has a property value in a numerical form associated with the given training example.
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The k-NN regression  algorithm uses all training sets to predict a property value for the given test sample.
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This predicted property value is an average of the values of its k nearest neighbors. If `k` is `1`, then the test sample is simply assigned to the property value of a single nearest neighbor.
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Presently, Ignite supports a few parameters for k-NN regression algorithm:
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* `k` - a number of nearest neighbors
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* `distanceMeasure` - one of the distance metrics provided by the ML framework such as Euclidean, Hamming or Manhattan
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* `isWeighted` - false by default, if true it enables a weighted KNN algorithm.
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* `dataCache` -  holds a training set of objects for which the class is already known.
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* `indexType` - distributed spatial index, has three values: ARRAY, KD_TREE, BALL_TREE
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[source, java]
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----
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// Create trainer
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KNNRegressionTrainer trainer = new KNNRegressionTrainer()
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  .withK(5)
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  .withIdxType(SpatialIndexType.BALL_TREE)
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  .withDistanceMeasure(new ManhattanDistance())
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  .withWeighted(true);
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// Train model.
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KNNClassificationModel knnMdl = trainer.fit(
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  ignite,
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  dataCache,
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  vectorizer
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);
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// Make a prediction.
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double prediction = knnMdl.predict(observation);
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----
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== Example
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To see how kNN Regression can be used in practice, try this https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java[example^] that is available on GitHub and delivered with every Apache Ignite distribution.
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The training dataset is the Iris dataset which can be loaded from the https://archive.ics.uci.edu/ml/datasets/iris[UCI Machine Learning Repository^].
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