js 线性度 CORREL 的实现

问题

需要用 js 来实现 线性度 CORREL.

解决

一组是固定值的线性度计算

比如: {0,10,30,60,80} 是一组固定的数据集。

<template>
  <div>
    <h1>Linearity (Correlation Coefficient) Calculator</h1>
    <textarea v-model="inputData" placeholder="Enter data for set B (comma-separated, e.g., 1,2,3,4,5)"></textarea>
    <button @click="calculateLinearity">Calculate Linearity</button>
    <p>Correlation Coefficient: {{ correlationCoefficient.toFixed(4) }}</p>
  </div>
</template>

<script>
export default {
  data() {
    return {
      fixedData: [0, 10, 30, 60, 80], // 固定数据集
      inputData: '', // 用户输入的数据集
      correlationCoefficient: null, // 相关系数
    };
  },
  methods: {
    parseArray(dataString) {
      // 将逗号分隔的字符串转换为数字数组
      return dataString.split(',').map(Number).filter(num => !isNaN(num));
    },
    calculateMean(array) {
      // 计算数组的平均值
      return array.reduce((acc, val) => acc + val, 0) / array.length;
    },
    calculateCovariance(array1, array2, mean1, mean2) {
      // 计算两个数组的协方差
      const n = array1.length;
      let covariance = 0;
      for (let i = 0; i < n; i++) {
        covariance += (array1[i] - mean1) * (array2[i] - mean2);
      }
      return covariance / (n - 1); // 使用 n-1 作为分母（样本协方差）
    },
    calculateStandardDeviation(array, mean) {
      // 计算数组的标准差
      const n = array.length;
      let variance = 0;
      for (let i = 0; i < n; i++) {
        variance += Math.pow(array[i] - mean, 2);
      }
      return Math.sqrt(variance / (n - 1)); // 使用 n-1 作为分母（样本标准差）
    },
    calculateCorrelationCoefficient(array1, array2) {
      // 计算两个数组的相关系数
      const mean1 = this.calculateMean(array1);
      const mean2 = this.calculateMean(array2);
      const stdDev1 = this.calculateStandardDeviation(array1, mean1);
      const stdDev2 = this.calculateStandardDeviation(array2, mean2);
      const covariance = this.calculateCovariance(array1, array2, mean1, mean2);
      return covariance / (stdDev1 * stdDev2);
    },
    calculateLinearity() {
      // 解析用户输入的数据字符串为数组
      const parsedInput = this.parseArray(this.inputData);

      // 检查数组长度是否相等
      if (parsedInput.length !== this.fixedData.length) {
        alert('The number of elements in both data sets must be equal.');
        return;
      }

      // 计算相关系数
      this.correlationCoefficient = this.calculateCorrelationCoefficient(this.fixedData, parsedInput);
    },
  },
};
</script>

<style scoped>
textarea {
  width: 100%;
  height: 50px;
  margin-bottom: 10px;
}
button {
  margin-top: 10px;
}
</style>

两个变量集

<template>
  <div>
    <h1>Correlation Coefficient Calculator</h1>
    <textarea v-model="dataA" placeholder="Enter data for set A (comma-separated)"></textarea>
    <textarea v-model="dataB" placeholder="Enter data for set B (comma-separated)"></textarea>
    <button @click="calculateCorrelation">Calculate Correlation</button>
    <p>Correlation Coefficient: {{ correlationCoefficient }}</p>
  </div>
</template>

<script>
export default {
  data() {
    return {
      dataA: '',
      dataB: '',
      correlationCoefficient: null,
    };
  },
  methods: {
    parseArray(dataString) {
      // 将逗号分隔的字符串转换为数字数组
      return dataString.split(',').map(Number).filter(num => !isNaN(num));
    },
    calculateMean(array) {
      // 计算数组的平均值
      const sum = array.reduce((acc, val) => acc + val, 0);
      return sum / array.length;
    },
    calculateStandardDeviation(array, mean) {
      // 计算数组的标准差
      const variance = array.reduce((acc, val) => acc + Math.pow(val - mean, 2), 0) / array.length;
      return Math.sqrt(variance);
    },
    calculateCorrelation(arrayA, arrayB, meanA, meanB, stdDevA, stdDevB) {
      // 计算相关系数
      const covariance = arrayA.reduce((acc, valA, index) => acc + (valA - meanA) * (arrayB[index] - meanB), 0);
      return covariance / (stdDevA * stdDevB);
    },
    calculateCorrelationCoefficient() {
      // 解析数据字符串为数组
      const parsedA = this.parseArray(this.dataA);
      const parsedB = this.parseArray(this.dataB);

      // 检查数组长度是否相等
      if (parsedA.length !== parsedB.length) {
        alert('Data sets must have the same number of elements.');
        return;
      }

      // 计算平均值和标准差
      const meanA = this.calculateMean(parsedA);
      const meanB = this.calculateMean(parsedB);
      const stdDevA = this.calculateStandardDeviation(parsedA, meanA);
      const stdDevB = this.calculateStandardDeviation(parsedB, meanB);

      // 计算相关系数
      this.correlationCoefficient = this.calculateCorrelation(parsedA, parsedB, meanA, meanB, stdDevA, stdDevB);
    },
  },
};
</script>

<style scoped>
textarea {
  width: 100%;
  height: 100px;
  margin-bottom: 10px;
}
button {
  margin-top: 10px;
}
</style>