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WIP update-aggregate-funcions-in-zh
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benbiti
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@ -4,8 +4,12 @@
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## 保持英文，不译
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Parquet
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## 英文 <-> 中文
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Tuple 元组
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## 英文 <-> 中文
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Integer 整数
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floating-point 浮点数
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Decimal 定点数
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Tuple 元组
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function 函数
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@ -379,47 +379,6 @@ kurtSamp(expr)
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SELECT kurtSamp(value) FROM series_with_value_column
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```
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## avgWeighted {#avgweighted}
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计算 [加权算术平均值](https://en.wikipedia.org/wiki/Weighted_arithmetic_mean).
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**语法**
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``` sql
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avgWeighted(x, weight)
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```
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**参数**
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- `x` — 值。 [整数](../data-types/int-uint.md) 或 [浮点](../data-types/float.md).
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- `weight` — 值的加权。 [整数](../data-types/int-uint.md) 或 [浮点](../data-types/float.md).
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`x` 和 `weight` 的类型一定是一样的
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**返回值**
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- 加权平均值。
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- `NaN`. 如果所有的权重都等于0。
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类型: [Float64](../data-types/float.md).
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**示例**
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查询:
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``` sql
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SELECT avgWeighted(x, w)
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FROM values('x Int8, w Int8', (4, 1), (1, 0), (10, 2))
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```
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结果:
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``` text
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┌─avgWeighted(x, weight)─┐
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│ 8 │
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└────────────────────────┘
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```
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## uniq {#agg_function-uniq}
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计算参数的不同值的近似数量。
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@ -4,42 +4,43 @@ toc_priority: 107
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# avgWeighted {#avgweighted}
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Calculates the [weighted arithmetic mean](https://en.wikipedia.org/wiki/Weighted_arithmetic_mean).
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**Syntax**
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计算 [加权算术平均值](https://en.wikipedia.org/wiki/Weighted_arithmetic_mean).
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**语法**
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``` sql
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avgWeighted(x, weight)
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```
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**Parameters**
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**参数**
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- `x` — Values.
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- `weight` — Weights of the values.
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- `x` — 值。
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- `weight` — 值的加权。
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`x` and `weight` must both be
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[Integer](../../../sql-reference/data-types/int-uint.md),
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[floating-point](../../../sql-reference/data-types/float.md), or
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[Decimal](../../../sql-reference/data-types/decimal.md),
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but may have different types.
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`x` 和 `weight` 的类型必须是
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[整数](../../../sql-reference/data-types/int-uint.md), 或
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[浮点数](../../../sql-reference/data-types/float.md), 或
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[定点数](../../../sql-reference/data-types/decimal.md),
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但是可以不一样。
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**Returned value**
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**返回值**
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- `NaN` if all the weights are equal to 0 or the supplied weights parameter is empty.
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- Weighted mean otherwise.
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- `NaN`。 如果所有的权重都等于0 或所提供的权重参数是空。
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- 加权平均值。 其他。
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**Return type** is always [Float64](../../../sql-reference/data-types/float.md).
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类型: 总是[Float64](../data-types/float.md).
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**Example**
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**示例**
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Query:
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查询:
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``` sql
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SELECT avgWeighted(x, w)
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FROM values('x Int8, w Int8', (4, 1), (1, 0), (10, 2))
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```
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Result:
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结果:
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``` text
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┌─avgWeighted(x, weight)─┐
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@ -47,33 +48,17 @@ Result:
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└────────────────────────┘
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```
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**Example**
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Query:
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**示例**
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``` sql
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SELECT avgWeighted(x, w)
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FROM values('x Int8, w Float64', (4, 1), (1, 0), (10, 2))
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```
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Result:
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``` text
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┌─avgWeighted(x, weight)─┐
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│ 8 │
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└────────────────────────┘
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```
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**Example**
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Query:
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查询:
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``` sql
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SELECT avgWeighted(x, w)
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FROM values('x Int8, w Int8', (0, 0), (1, 0), (10, 0))
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```
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Result:
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结果:
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``` text
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┌─avgWeighted(x, weight)─┐
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└────────────────────────┘
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```
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**Example**
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**示例**
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Query:
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查询:
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``` sql
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CREATE table test (t UInt8) ENGINE = Memory;
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SELECT avgWeighted(t) FROM test
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```
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Result:
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结果:
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``` text
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┌─avgWeighted(x, weight)─┐
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@ -4,10 +4,10 @@ toc_priority: 250
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# categoricalInformationValue {#categoricalinformationvalue}
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Calculates the value of `(P(tag = 1) - P(tag = 0))(log(P(tag = 1)) - log(P(tag = 0)))` for each category.
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对于每个类别计算 `(P(tag = 1) - P(tag = 0))(log(P(tag = 1)) - log(P(tag = 0)))` 。
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``` sql
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categoricalInformationValue(category1, category2, ..., tag)
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```
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The result indicates how a discrete (categorical) feature `[category1, category2, ...]` contribute to a learning model which predicting the value of `tag`.
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结果指示离散（分类）要素如何使用 `[category1, category2, ...]` 有助于使用学习模型预测`tag`的值。
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# corr {#corrx-y}
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Syntax: `corr(x, y)`
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**语法**
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``` sql
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`corr(x, y)`
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```
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Calculates the Pearson correlation coefficient: `Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)^2) * Σ((y - y̅)^2))`.
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计算Pearson相关系数: `Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)^2) * Σ((y - y̅)^2))`。
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!!! note "Note"
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This function uses a numerically unstable algorithm. If you need [numerical stability](https://en.wikipedia.org/wiki/Numerical_stability) in calculations, use the `corrStable` function. It works slower but provides a lower computational error.
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!!! note "注"
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该函数使用数值不稳定的算法。 如果你需要 [数值稳定性](https://en.wikipedia.org/wiki/Numerical_stability) 在计算中，使用 `corrStable` 函数。 它的工作速度较慢，但提供较低的计算错误。
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# count {#agg_function-count}
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Counts the number of rows or not-NULL values.
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ClickHouse supports the following syntaxes for `count`:
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- `count(expr)` or `COUNT(DISTINCT expr)`.
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- `count()` or `COUNT(*)`. The `count()` syntax is ClickHouse-specific.
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计数行数或非空值。
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**Parameters**
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ClickHouse支持以下 `count` 语法:
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- `count(expr)` 或 `COUNT(DISTINCT expr)`。
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- `count()` 或 `COUNT(*)`. 该 `count()` 语法是ClickHouse特定的。
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The function can take:
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**参数**
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- Zero parameters.
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- One [expression](../../../sql-reference/syntax.md#syntax-expressions).
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该函数可以采取:
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**Returned value**
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- 零参数。
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- 一个 [表达式](../../../sql-reference/syntax.md#syntax-expressions)。
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- If the function is called without parameters it counts the number of rows.
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- If the [expression](../../../sql-reference/syntax.md#syntax-expressions) is passed, then the function counts how many times this expression returned not null. If the expression returns a [Nullable](../../../sql-reference/data-types/nullable.md)-type value, then the result of `count` stays not `Nullable`. The function returns 0 if the expression returned `NULL` for all the rows.
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**返回值**
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In both cases the type of the returned value is [UInt64](../../../sql-reference/data-types/int-uint.md).
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- 如果没有参数调用函数，它会计算行数。
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- 如果 [表达式](../../../syntax.md#syntax-expressions) 被传递，则该函数计数此表达式返回非null的次数。 如果表达式返回 [可为空](../../../sql-reference/data-types/nullable.md)类型的值，`count`的结果仍然不 `Nullable`。 如果表达式对于所有的行都返回 `NULL` ，则该函数返回 0 。
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**Details**
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在这两种情况下，返回值的类型为 [UInt64](../../../sql-reference/data-types/int-uint.md)。
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ClickHouse supports the `COUNT(DISTINCT ...)` syntax. The behavior of this construction depends on the [count_distinct_implementation](../../../operations/settings/settings.md#settings-count_distinct_implementation) setting. It defines which of the [uniq\*](../../../sql-reference/aggregate-functions/reference/uniq.md#agg_function-uniq) functions is used to perform the operation. The default is the [uniqExact](../../../sql-reference/aggregate-functions/reference/uniqexact.md#agg_function-uniqexact) function.
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**详细信息**
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The `SELECT count() FROM table` query is not optimized, because the number of entries in the table is not stored separately. It chooses a small column from the table and counts the number of values in it.
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ClickHouse支持 `COUNT(DISTINCT ...)` 语法，这种结构的行为取决于 [count_distinct_implementation](../../../operations/settings/settings.md#settings-count_distinct_implementation) 设置。 它定义了用于执行该操作的 [uniq\*](../../../sql-reference/aggregate-functions/reference/uniq.md#agg_function-uniq)函数。 默认值是 [uniqExact](../../../sql-reference/aggregate-functions/reference/uniqexact.md#agg_function-uniqexact)函数。
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**Examples**
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`SELECT count() FROM table` 这个查询未被优化，因为表中的条目数没有单独存储。 它从表中选择一个小列并计算其值的个数。
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Example 1:
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**示例**
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示例1:
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``` sql
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SELECT count() FROM t
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@ -44,7 +45,7 @@ SELECT count() FROM t
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└─────────┘
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```
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Example 2:
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示例2:
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``` sql
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SELECT name, value FROM system.settings WHERE name = 'count_distinct_implementation'
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└────────────────┘
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```
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This example shows that `count(DISTINCT num)` is performed by the `uniqExact` function according to the `count_distinct_implementation` setting value.
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这个例子表明 `count(DISTINCT num)` 是通过 `count_distinct_implementation` 的设定值 `uniqExact` 函数来执行的。
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# covarPop {#covarpop}
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Syntax: `covarPop(x, y)`
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**语法**
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``` sql
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`covarPop(x, y)`
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```
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Calculates the value of `Σ((x - x̅)(y - y̅)) / n`.
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计算 `Σ((x - x̅)(y - y̅)) / n` 的值。
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!!! note "Note"
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This function uses a numerically unstable algorithm. If you need [numerical stability](https://en.wikipedia.org/wiki/Numerical_stability) in calculations, use the `covarPopStable` function. It works slower but provides a lower computational error.
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!!! note "注"
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该函数使用数值不稳定的算法。 如果你需要 [数值稳定性](https://en.wikipedia.org/wiki/Numerical_stability) 在计算中，使用 `covarPopStable` 函数。 它的工作速度较慢，但提供了较低的计算错误。
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# covarSamp {#covarsamp}
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Calculates the value of `Σ((x - x̅)(y - y̅)) / (n - 1)`.
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**语法**
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``` sql
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`covarSamp(x, y)`
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```
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Returns Float64. When `n <= 1`, returns +∞.
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计算 `Σ((x - x̅)(y - y̅)) / (n - 1)` 的值。
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!!! note "Note"
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This function uses a numerically unstable algorithm. If you need [numerical stability](https://en.wikipedia.org/wiki/Numerical_stability) in calculations, use the `covarSampStable` function. It works slower but provides a lower computational error.
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返回Float64。 当 `n <= 1`, 返回 +∞。
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!!! note "注"
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该函数使用数值不稳定的算法。 如果你需要 [数值稳定性](https://en.wikipedia.org/wiki/Numerical_stability) 在计算中，使用 `covarSampStable` 函数。 它的工作速度较慢，但提供较低的计算错误。
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# groupArray {#agg_function-grouparray}
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Syntax: `groupArray(x)` or `groupArray(max_size)(x)`
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**语法**
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``` sql
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`groupArray(x)`
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Creates an array of argument values.
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Values can be added to the array in any (indeterminate) order.
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or
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The second version (with the `max_size` parameter) limits the size of the resulting array to `max_size` elements. For example, `groupArray(1)(x)` is equivalent to `[any (x)]`.
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`groupArray(max_size)(x)`
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```
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In some cases, you can still rely on the order of execution. This applies to cases when `SELECT` comes from a subquery that uses `ORDER BY`.
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创建参数值的数组。
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值可以按任何（不确定）顺序添加到数组中。
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第二个版本（带有 `max_size` 参数）将结果数组的大小限制为 `max_size` 个元素。
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例如, `groupArray (1) (x)` 相当于 `[any (x)]` 。
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在某些情况下，您仍然可以依赖执行顺序。这适用于SELECT(查询)来自使用了 `ORDER BY` 子查询的情况。
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