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‎src/binning/README.md‎

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# 数值分类
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# Binning
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**Read this in other languages: [简体中文](./README.zh-CN.md)**
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本包主要封装数值分类相关接口.
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This package mainly encapsulates interfaces related to numerical classification.
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## 结构体
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## Structure
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```go
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package binning
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type Calculator struct {
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width int32 // 等宽分组参数: 总共分组的组别个数
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freq int32 // 等频分组参数: 每组中包含的数值个数
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kMeansMaxIter int32 // 聚类分组参数: KMeans算法最大迭代次数
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kMeansK int32 //聚类分组参数: KMeans算法k值
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width int32 // Equal width grouping parameter: the total number of groups
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freq int32 // Equal frequency grouping parameter: the number of values ​​contained in each group
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kMeansMaxIter int32 // Clustering grouping parameter: the maximum number of iterations of the KMeans algorithm
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kMeansK int32 // Clustering grouping parameter: the k value of the KMeans algorithm
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}
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```
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可以调用 ```func NewCalculator(optionalParam ...int32) Calculator``` 获取对象.
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动态参数如果长度等于4则会以 width, freq, kMeansMaxIter, kMeansK 的顺序进行初始化, 如果长度不为4则自动初始化为默认参数.
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默认参数信息详见文件 ```src/static.go```
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## 接口
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1. **ClusterWithKMeansInt32([]int32) ([]int32, error)**
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```text
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使用K-Means算法对数据进行聚类分组.
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注意:
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1. 输入数据长度不可为0.
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2. k值不可大于数据点的数量.
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```
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2. **ClusterWithKMeansFloat32([]float32) ([]int32, error)**
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```text
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使用K-Means算法对数据进行聚类分组.
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注意:
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1. 输入数据长度不可为0.
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2. k值不可大于数据点的数量.
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```
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3. **EqualWidthBinningInt32([]int32) ([]int32, error)**
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```text
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对数据进行等宽分组, 分组数量为Calculator.width
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返回每个数据点的区间索引.
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注意: 输入的数据长度不得为0
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```
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4. **EqualWidthBinningFloat32([]float32) ([]int32, error)**
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```text
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对数据进行等宽分组, 分组数量为Calculator.width
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返回每个数据点的区间索引.
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注意: 输入的数据长度不得为0
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```
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5. **EqualFrequencyBinningInt32([]int32) ([]int32, error)**
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```text
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对数据进行等频分组, 每组数据个数为Calculator.freq
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返回每个数据点的区间索引.
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```
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6. **EqualFrequencyBinningFloat32([]float32) ([]int32, error)**
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```text
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对数据进行等频分组, 每组数据个数为Calculator.freq
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返回每个数据点的区间索引.
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```
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You can call ```func NewCalculator(optionalParam ...int32) Calculator``` to get the object.
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If the length of the dynamic parameter is equal to 4, it will be initialized in the order of width, freq, kMeansMaxIter,
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kMeansK. If the length is not 4, it will be automatically initialized to the default parameter.
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For details about the default parameters, see the file ```src/static.go```
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## Interface
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### 1.ClusterWithKMeansInt32([]int32) ([]int32, error)
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Use the K-Means algorithm to cluster and group data. The slice index returned by the function corresponds to the input
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slice, and the slice element is the group number of the value of the index.
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> [!IMPORTANT]
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> 1. The input data length cannot be 0.
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> 2. The k value cannot be greater than the number of data points.
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### 2. ClusterWithKMeansFloat32([]float32) ([]int32, error)
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Use the K-Means algorithm to cluster and group data. The slice index returned by the function corresponds to the input
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slice, and the slice element is the group number of the value of the index.
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> [!IMPORTANT]
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> 1. The input data length cannot be 0.
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> 2. The k value cannot be greater than the number of data points.
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### 3. EqualWidthBinningInt32([]int32) ([]int32, error)
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Group the data with equal width, the number of groups is Calculator.width. The function returns the interval index of
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each data point.
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> [!IMPORTANT]
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> The length of the input data cannot be 0
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### 4. EqualWidthBinningFloat32([]float32) ([]int32, error)
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Group the data with equal width, the number of groups is Calculator.width. The function returns the interval index of
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each data point.
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> [!IMPORTANT]
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> The length of the input data cannot be 0
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### 5. EqualFrequencyBinningInt32([]int32) ([]int32, error)
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Group the data with equal frequency, the number of data in each group is Calculator.freq. The function returns the
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interval index of each data point.
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### 6. EqualFrequencyBinningFloat32([]float32) ([]int32, error)
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Group the data with equal frequency, The number of data points in each group is Calculator.freq. The function returns
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the interval index of each data point.

‎src/binning/README.zh-CN.md‎

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# 数值分类
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**其他语言版本: [English](./README.md)**
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本包主要封装数值分类相关接口.
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## 结构体
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```go
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package binning
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type Calculator struct {
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width int32 // 等宽分组参数: 总共分组的组别个数
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freq int32 // 等频分组参数: 每组中包含的数值个数
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kMeansMaxIter int32 // 聚类分组参数: KMeans算法最大迭代次数
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kMeansK int32 // 聚类分组参数: KMeans算法k值
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}
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```
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可以调用 ```func NewCalculator(optionalParam ...int32) Calculator``` 获取对象.
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动态参数如果长度等于4则会以 width, freq, kMeansMaxIter, kMeansK 的顺序进行初始化, 如果长度不为4则自动初始化为默认参数.
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默认参数信息详见文件 ```src/static.go```
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## 接口
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### 1.ClusterWithKMeansInt32([]int32) ([]int32, error)
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使用K-Means算法对数据进行聚类分组. 函数返回的切片下标与输入切片对应, 切片元素为该索引的值的分组组号.
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> [!IMPORTANT]
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> 1. 输入数据长度不可为0.
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> 2. k值不可大于数据点的数量.
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### 2. ClusterWithKMeansFloat32([]float32) ([]int32, error)
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使用K-Means算法对数据进行聚类分组. 函数返回的切片下标与输入切片对应, 切片元素为该索引的值的分组组号.
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> [!IMPORTANT]
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> 1. 输入数据长度不可为0.
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> 2. k值不可大于数据点的数量.
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### 3. EqualWidthBinningInt32([]int32) ([]int32, error)
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对数据进行等宽分组, 分组数量为Calculator.width. 函数返回每个数据点的区间索引.
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> [!IMPORTANT]
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> 输入的数据长度不得为0
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### 4. EqualWidthBinningFloat32([]float32) ([]int32, error)
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对数据进行等宽分组, 分组数量为Calculator.width. 函数返回每个数据点的区间索引.
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> [!IMPORTANT]
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> 输入的数据长度不得为0
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### 5. EqualFrequencyBinningInt32([]int32) ([]int32, error)
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对数据进行等频分组, 每组数据个数为Calculator.freq. 函数返回每个数据点的区间索引.
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### 6. EqualFrequencyBinningFloat32([]float32) ([]int32, error)
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对数据进行等频分组, 每组数据个数为Calculator.freq. 函数返回每个数据点的区间索引.
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‎src/conversion/README.md‎

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# 数值转换
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# Conversion
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**Read this in other languages: [简体中文](./README.zh-CN.md)**
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本包主要封装数值转换相关接口.
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This package mainly encapsulates interfaces related to numerical conversion.
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## 结构体
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## Structure
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```go
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package conversion
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type Calculator struct {
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logBase float32 //对数计算的底数, 默认为自然数e
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logBase float32 //The base of logarithmic calculation, defaults to natural number e
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}
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```
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可以调用 ```func NewCalculator(optionalParam ...float32) Calculator ``` 获取对象. 动态参数如果存在则设置为logBase, 若不存在则默认设置为自然数e.
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## 接口
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1. **StandardizationInt32([]int32) ([]float32, error)**
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```text
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最大最小值标准化.
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将int32类型的一组数据处理过后数据均落在［0，1］区间内, 处理后输出不再是整数.
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计算公式为: normalized=(maxValue−minValue)/(value−minValue)
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注意: 无法对零范围(最大最小值相等)的数组进行规范化.
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```
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2. **StandardizationFloat32([]float32) ([]float32, error)**
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```text
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最大最小值标准化.
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将float32类型的一组数据处理过后数据均落在［0，1］区间内.
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计算公式为: normalized=(maxValue−minValue)/(value−minValue)
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注意: 无法对零范围(最大最小值相等)的数组进行规范化.
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```
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3. **ZScoreInt32([]int32) ([]float32, error)**
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```text
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Z-Score标准化.
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计算公式为: Z=(X−μ)/σ
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X 是原始数据, μ 是数据的平均值, σ 是数据的标准差
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注意: 无法对零范围(最大最小值相等)的数组进行规范化.
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```
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4. **ZScoreFloat32([]float32) ([]float32, error)**
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```text
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Z-Score标准化.
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计算公式为: Z=(X−μ)/σ
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X 是原始数据, μ 是数据的平均值, σ 是数据的标准差
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注意: 无法对零范围(最大最小值相等)的数组进行规范化.
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```
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5. **LogInt32([]int32) ([]float32, error)**
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```text
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计算对数.
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计算公式为: log(base, x), base默认为自然数e.
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注意: 需保证输入参数均为正数, 否则:
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Log(+Inf) = +Inf
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Log(0) = -Inf
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Log(x < 0) = NaN
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Log(NaN) = NaN
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```
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6. **LogFloat32([]float32) ([]float32, error)**
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```text
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计算对数.
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计算公式为: log(base, x), base默认为自然数e.
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注意: 需保证输入参数均为正数, 否则:
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Log(+Inf) = +Inf
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Log(0) = -Inf
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Log(x < 0) = NaN
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Log(NaN) = NaN
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```
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7. **SquareInt32([]int32) ([]float32, error)**
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```text
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计算平方根.
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计算公式为: √x
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注意: 需保证输入参数均为非负实数, 否则:
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Sqrt(+Inf) = +Inf
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Sqrt(±0) = ±0
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Sqrt(x < 0) = NaN
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Sqrt(NaN) = NaN
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```
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8. **SquareFloat32([]float32) ([]float32, error)**
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```text
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计算平方根.
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计算公式为: √x
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注意: 需保证输入参数均为非负实数, 否则:
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Sqrt(+Inf) = +Inf
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Sqrt(±0) = ±0
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Sqrt(x < 0) = NaN
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Sqrt(NaN) = NaN
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```
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You can call ```func NewCalculator(optionalParam ...float32) Calculator ``` to get the object. If the dynamic parameter exists, it is set to logBase. If it does not exist, it is set to natural number e by default.
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## Interface
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### 1. StandardizationInt32([]int32) ([]float32, error)
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Maximum and minimum value standardization.
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After processing a set of int32 type data, the data all fall within the range of [0, 1]. After processing, the output is no longer an integer.
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The calculation formula is:
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$$normalized=(maxValue−minValue)/(value−minValue)$$
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> [!IMPORTANT]
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> It is not possible to normalize an array with zero range (equal maximum and minimum values).
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### 2. StandardizationFloat32([]float32) ([]float32, error)
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Maximum and minimum value normalization.
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After processing a set of float32 data, the data all fall within the interval [0, 1].
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The calculation formula is:
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$$normalized=(maxValue−minValue)/(value−minValue)$$
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> [!IMPORTANT]
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> It is not possible to normalize an array with zero range (equal maximum and minimum values).
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### 3. ZScoreInt32([]int32) ([]float32, error)
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Z-Score standardization.
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The calculation formula is:
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$$Z=(X−\mu)/\sigma$$
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$X$ is the original data, $\mu$ is the mean value of the data, and $\sigma$ is the standard deviation of the data.
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> [!IMPORTANT]
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> Cannot normalize an array with zero range (equal maximum and minimum values).
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### 4. ZScoreFloat32([]float32) ([]float32, error)
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Z-Score normalization.
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The calculation formula is:
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$$Z=(X−\mu)/\sigma$$
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$X$ is the original data, $\mu$ is the mean value of the data, and $\sigma$ is the standard deviation of the data.
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### 5. LogInt32([]int32) ([]float32, error)
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Calculate the logarithm.
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The calculation formula is:
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$$log(base, x)$$
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> [!TIP]
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> base defaults to the natural number e
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> [!IMPORTANT]
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> Make sure that all input parameters are positive, otherwise:
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> Log(+Inf) = +Inf
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> Log(0) = -Inf
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> Log(x < 0) = NaN
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> Log(NaN) = NaN
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### 6. LogFloat32([]float32) ([]float32, error)
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Calculates logarithm.
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Calculation formula:
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$$log(base, x)$$
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> [!TIP]
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> base defaults to natural number e
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> [!IMPORTANT]
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> Input parameters must be positive, otherwise:
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> Log(+Inf) = +Inf
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> Log(0) = -Inf
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> Log(x < 0) = NaN
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> Log(NaN) = NaN
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### 7. SquareInt32([]int32) ([]float32, error)
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Calculates square root.
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Calculation formula:
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$$\sqrt{x}$$
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> [!IMPORTANT]
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> Input parameters must be non-negative real numbers, otherwise:
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> Sqrt(+Inf) = +Inf
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> Sqrt(±0) = ±0
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> Sqrt(x < 0) = NaN
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> Sqrt(NaN) = NaN
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### 8. SquareFloat32([]float32) ([]float32, error)
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Calculate the square root.
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The calculation formula is:
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$$\sqrt{x}$$
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> [!IMPORTANT]
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> Ensure that all input parameters are non-negative real numbers, otherwise:
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> Sqrt(+Inf) = +Inf
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> Sqrt(±0) = ±0
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> Sqrt(x < 0) = NaN
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> Sqrt(NaN) = NaN

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