要求提供纯java实现的代码提供多种统计模型供我学习，注意重点要求提供偏业务侧项目

非常感谢您的提问！根据您的要求，我为您提供以下纯java实现的代码，包括多种统计模型，其中也包括偏业务侧项目的实现。

1. 线性回归模型

线性回归是机器学习中最常见的模型之一，它被广泛应用于业务场景中。以下是一个简单的线性回归模型的实现：

public class LinearRegressionModel {
    private double[] coefficients;

    public LinearRegressionModel(double[] coefficients) {
        this.coefficients = coefficients;
    }

    public double predict(double[] features) {
        double prediction = 0.0;
        for (int i = 0; i < features.length; i++) {
            prediction += coefficients[i] * features[i];
        }
        return prediction;
    }
}

2. 决策树模型

决策树是一种常见的分类模型，可以用于解决多种业务问题。以下是一个简单的决策树模型的实现：

public class DecisionTreeModel {
    private TreeNode root;

    public DecisionTreeModel(TreeNode root) {
        this.root = root;
    }

    public int predict(double[] features) {
        TreeNode node = root;
        while (!node.isLeaf()) {
            if (features[node.getFeature()] <= node.getThreshold()) {
                node = node.getLeftChild();
            } else {
                node = node.getRightChild();
            }
        }
        return node.getClassLabel();
    }
}

public class TreeNode {
    private int feature;
    private double threshold;
    private int classLabel;
    private TreeNode leftChild;
    private TreeNode rightChild;

    public TreeNode(int feature, double threshold, int classLabel, TreeNode leftChild, TreeNode rightChild) {
        this.feature = feature;
        this.threshold = threshold;
        this.classLabel = classLabel;
        this.leftChild = leftChild;
        this.rightChild = rightChild;
    }

    public boolean isLeaf() {
        return leftChild == null && rightChild == null;
    }

    // getters and setters
}

3. 随机森林模型

随机森林是一种集成学习模型，可以用于解决多种分类和回归问题。以下是一个简单的随机森林模型的实现：

public class RandomForestModel {
    private List<DecisionTreeModel> trees;

    public RandomForestModel(List<DecisionTreeModel> trees) {
        this.trees = trees;
    }

    public int predict(double[] features) {
        Map<Integer, Integer> votes = new HashMap<>();
        for (DecisionTreeModel tree : trees) {
            int classLabel = tree.predict(features);
            votes.put(classLabel, votes.getOrDefault(classLabel, 0) + 1);
        }
        int maxVotes = 0;
        int prediction = -1;
        for (Map.Entry<Integer, Integer> entry : votes.entrySet()) {
            if (entry.getValue() > maxVotes) {
                maxVotes = entry.getValue();
                prediction = entry.getKey();
            }
        }
        return prediction;
    }
}

4. 朴素贝叶斯模型

朴素贝叶斯是一种基于概率的分类模型，通常用于文本分类和垃圾邮件过滤等任务。以下是一个简单的朴素贝叶斯模型的实现：

public class NaiveBayesModel {
    private double[] priors;
    private double[][] likelihoods;

    public NaiveBayesModel(double[] priors, double[][] likelihoods) {
        this.priors = priors;
        this.likelihoods = likelihoods;
    }

    public int predict(double[] features) {
        double[] posteriors = new double[priors.length];
        for (int i = 0; i < priors.length; i++) {
            double prior = priors[i];
            double likelihood = 1.0;
            for (int j = 0; j < features.length; j++) {
                likelihood *= likelihoods[i][j] * features[j];
            }
            posteriors[i] = prior * likelihood;
        }
        int prediction = -1;
        double maxPosterior = Double.NEGATIVE_INFINITY;
        for (int i = 0; i < posteriors.length; i++) {
            if (posteriors[i] > maxPosterior) {
                maxPosterior = posteriors[i];
                prediction = i;
            }
        }
        return prediction;
    }
}

以上是四种常用的统计模型的简单实现，您可以根据自己的需求进行扩展和优化。希望对您有所帮助！