| Name | rust-pyfunc JSON |
| Version |
0.5.1
JSON |
| download |
| home_page | None |
| Summary | A collection of high-performance Python functions implemented in Rust |
| upload_time | 2024-11-22 08:26:16 |
| maintainer | None |
| docs_url | None |
| author | chenzongwei |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
python
rust
algorithms
performance
|
| VCS |
 |
| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Rust_Pyfunc
一些用Python计算起来很慢的指标，这里用Rust来实现，提升计算速度。
## 安装
```shell
pip install rust_pyfunc
```
## 使用
```python
import rust_pyfunc as rp
```
## 功能列表
### 1. 时间序列分析
#### 1.1 DTW动态时间规整 (dtw_distance)
计算两个时间序列之间的DTW（动态时间规整）距离。
```python
import rust_pyfunc as rp
# 示例数据
a = [1, 2, 3, 4]
b = [3, 9, 8, 6, 5]
# 计算DTW距离
distance = rp.dtw_distance(a, b)
print(f"DTW距离: {distance}")
```
#### 1.2 转移熵 (transfer_entropy)
计算从序列x到序列y的转移熵，用于衡量时间序列之间的因果关系。
```python
import numpy as np
from rust_pyfunc import transfer_entropy
# 创建两个相关的时间序列
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
y = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) # y比x滞后一个时间步
# 计算转移熵
k = 2 # 考虑过去2个时间步
c = 4 # 将数据离散化为4个等级
te = transfer_entropy(x, y, k, c)
print(f"从x到y的转移熵: {te}")
# 反向计算
te_reverse = transfer_entropy(y, x, k, c)
print(f"从y到x的转移熵: {te_reverse}")
```
#### 1.3 趋势计算 (trend 和 trend_fast)
计算时间序列的趋势。
```python
from rust_pyfunc import trend, trend_fast
# 准备数据
data = [1.0, 2.1, 1.9, 3.2, 4.0, 3.8, 4.5]
# 使用trend函数（更准确但较慢）
trend_result = trend(data)
print(f"趋势（标准版）: {trend_result}")
# 使用trend_fast函数（更快但精度略低）
trend_fast_result = trend_fast(np.array(data,dtype=float))
print(f"趋势（快速版）: {trend_fast_result}")
```
### 2. 统计分析
#### 2.1 最小二乘回归 (ols 和 ols_predict)
执行最小二乘回归分析。
```python
from rust_pyfunc import ols, ols_predict
import numpy as np
# 准备数据
X = np.array([[1, 2], [3, 4], [5, 6]]) # 特征矩阵
y = np.array([2.1, 3.8, 5.2]) # 目标变量
# 训练模型
coefficients = ols(X, y)
print(f"回归系数: {coefficients}")
# 预测新数据
X_new = np.array([[2, 3], [4, 5]])
predictions = ols_predict(X,y, X_new)
print(f"预测结果: {predictions}")
```
#### 2.2 区间统计 (min_range_loop 和 max_range_loop)
计算滑动窗口内的最小值和最大值。
```python
from rust_pyfunc import min_range_loop, max_range_loop
# 准备数据
data = [1.0, 4.0, 2.0, 5.0, 3.0, 6.0, 2.0]
# 计算滑动窗口最小值
min_values = min_range_loop(data)
print(f"滑动窗口最小值: {min_values}")
# 计算滑动窗口最大值
max_values = max_range_loop(data)
print(f"滑动窗口最大值: {max_values}")
```
### 3. 文本分析
#### 3.1 句子向量化 (vectorize_sentences 和 vectorize_sentences_list)
将句子转换为词频向量。
```python
from rust_pyfunc import vectorize_sentences, vectorize_sentences_list
# 两个句子的向量化
s1 = "The quick brown fox"
s2 = "The lazy brown dog"
v1, v2 = vectorize_sentences(s1, s2)
print(f"句子1的词频向量: {v1}")
print(f"句子2的词频向量: {v2}")
# 多个句子的向量化
sentences = [
"The quick brown fox",
"The lazy brown dog",
"A quick brown fox jumps"
]
vectors = vectorize_sentences_list(sentences)
for i, vec in enumerate(vectors):
print(f"句子{i+1}的词频向量: {vec}")
```
#### 3.2 Jaccard相似度 (jaccard_similarity)
计算两个句子之间的Jaccard相似度。
```python
from rust_pyfunc import jaccard_similarity
# 测试完全相同的句子
s1 = "The quick brown fox"
s2 = "The quick brown fox"
sim1 = jaccard_similarity(s1, s2)
print(f"完全相同的句子相似度: {sim1}") # 输出: 1.0
# 测试部分相同的句子
s3 = "The lazy brown dog"
sim2 = jaccard_similarity(s1, s3)
print(f"部分相同的句子相似度: {sim2}") # 输出: 0.4
# 测试完全不同的句子
s4 = "Hello world example"
sim3 = jaccard_similarity(s1, s4)
print(f"完全不同的句子相似度: {sim3}") # 输出: 0.0
```
### 4. 序列分析
#### 4.1 分段识别 (identify_segments)
识别序列中的连续分段。
```python
from rust_pyfunc import identify_segments
# 准备数据
data = [1, 1, 1, 2, 2, 3, 3, 3, 1, 1]
# 识别连续分段
segments = identify_segments(data)
print(f"连续分段: {segments}")
# 输出形如: [(1, 3), (2, 2), (3, 3), (1, 2)]
# 表示：值1连续出现3次，值2连续出现2次，值3连续出现3次，值1连续出现2次
```
#### 4.2 最大范围乘积 (find_max_range_product)
寻找序列中乘积最大的区间。
```python
from rust_pyfunc import find_max_range_product
# 准备数据
data = [2.0, -3.0, 4.0, -1.0, 2.0, 1.0, -5.0, 4.0]
# 查找最大乘积区间
start_idx, end_idx, max_product = find_max_range_product(np.array(data,dtype=float))
print(f"最大乘积区间: [{start_idx}, {end_idx}]")
print(f"最大乘积值: {max_product}")
```
## 注意事项
1. 所有函数都经过Rust优化，相比Python原生实现有显著的性能提升
2. 输入数据需要符合函数要求的格式和类型
3. 部分函数（如`transfer_entropy`）的参数需要根据具体场景调整以获得最佳结果
4. 文本处理函数会自动进行大小写转换和标点符号处理
## 性能建议
1. 对于大规模数据，优先使用带有"fast"后缀的函数版本
2. 文本处理时，建议预先进行数据清洗
3. 时间序列分析时，注意数据的预处理（如归一化）可能会影响结果
## 贡献指南
欢迎提交Issue和Pull Request来改进这个项目。在提交代码前，请确保：
1. 代码经过充分测试
2. 添加了适当的文档和示例
3. 遵循项目的代码风格
## License
MIT License
Raw data
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"description": "# Rust_Pyfunc\n\n\u4e00\u4e9b\u7528Python\u8ba1\u7b97\u8d77\u6765\u5f88\u6162\u7684\u6307\u6807\uff0c\u8fd9\u91cc\u7528Rust\u6765\u5b9e\u73b0\uff0c\u63d0\u5347\u8ba1\u7b97\u901f\u5ea6\u3002\n\n## \u5b89\u88c5\n```shell\npip install rust_pyfunc\n```\n\n## \u4f7f\u7528\n```python\nimport rust_pyfunc as rp\n```\n\n## \u529f\u80fd\u5217\u8868\n\n### 1. \u65f6\u95f4\u5e8f\u5217\u5206\u6790\n\n#### 1.1 DTW\u52a8\u6001\u65f6\u95f4\u89c4\u6574 (dtw_distance)\n\u8ba1\u7b97\u4e24\u4e2a\u65f6\u95f4\u5e8f\u5217\u4e4b\u95f4\u7684DTW\uff08\u52a8\u6001\u65f6\u95f4\u89c4\u6574\uff09\u8ddd\u79bb\u3002\n\n```python\nimport rust_pyfunc as rp\n\n# \u793a\u4f8b\u6570\u636e\na = [1, 2, 3, 4]\nb = [3, 9, 8, 6, 5]\n\n# \u8ba1\u7b97DTW\u8ddd\u79bb\ndistance = rp.dtw_distance(a, b)\nprint(f\"DTW\u8ddd\u79bb: {distance}\")\n```\n\n#### 1.2 \u8f6c\u79fb\u71b5 (transfer_entropy)\n\u8ba1\u7b97\u4ece\u5e8f\u5217x\u5230\u5e8f\u5217y\u7684\u8f6c\u79fb\u71b5\uff0c\u7528\u4e8e\u8861\u91cf\u65f6\u95f4\u5e8f\u5217\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\u3002\n\n```python\nimport numpy as np\nfrom rust_pyfunc import transfer_entropy\n\n# \u521b\u5efa\u4e24\u4e2a\u76f8\u5173\u7684\u65f6\u95f4\u5e8f\u5217\nx = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])\ny = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) # y\u6bd4x\u6ede\u540e\u4e00\u4e2a\u65f6\u95f4\u6b65\n\n# \u8ba1\u7b97\u8f6c\u79fb\u71b5\nk = 2 # \u8003\u8651\u8fc7\u53bb2\u4e2a\u65f6\u95f4\u6b65\nc = 4 # \u5c06\u6570\u636e\u79bb\u6563\u5316\u4e3a4\u4e2a\u7b49\u7ea7\nte = transfer_entropy(x, y, k, c)\nprint(f\"\u4ecex\u5230y\u7684\u8f6c\u79fb\u71b5: {te}\")\n\n# \u53cd\u5411\u8ba1\u7b97\nte_reverse = transfer_entropy(y, x, k, c)\nprint(f\"\u4ecey\u5230x\u7684\u8f6c\u79fb\u71b5: {te_reverse}\")\n```\n\n#### 1.3 \u8d8b\u52bf\u8ba1\u7b97 (trend \u548c trend_fast)\n\u8ba1\u7b97\u65f6\u95f4\u5e8f\u5217\u7684\u8d8b\u52bf\u3002\n\n```python\nfrom rust_pyfunc import trend, trend_fast\n\n# \u51c6\u5907\u6570\u636e\ndata = [1.0, 2.1, 1.9, 3.2, 4.0, 3.8, 4.5]\n\n# \u4f7f\u7528trend\u51fd\u6570\uff08\u66f4\u51c6\u786e\u4f46\u8f83\u6162\uff09\ntrend_result = trend(data)\nprint(f\"\u8d8b\u52bf\uff08\u6807\u51c6\u7248\uff09: {trend_result}\")\n\n# \u4f7f\u7528trend_fast\u51fd\u6570\uff08\u66f4\u5feb\u4f46\u7cbe\u5ea6\u7565\u4f4e\uff09\ntrend_fast_result = trend_fast(np.array(data,dtype=float))\nprint(f\"\u8d8b\u52bf\uff08\u5feb\u901f\u7248\uff09: {trend_fast_result}\")\n```\n\n### 2. \u7edf\u8ba1\u5206\u6790\n\n#### 2.1 \u6700\u5c0f\u4e8c\u4e58\u56de\u5f52 (ols \u548c ols_predict)\n\u6267\u884c\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u5206\u6790\u3002\n\n```python\nfrom rust_pyfunc import ols, ols_predict\nimport numpy as np\n\n# \u51c6\u5907\u6570\u636e\nX = np.array([[1, 2], [3, 4], [5, 6]]) # \u7279\u5f81\u77e9\u9635\ny = np.array([2.1, 3.8, 5.2]) # \u76ee\u6807\u53d8\u91cf\n\n# \u8bad\u7ec3\u6a21\u578b\ncoefficients = ols(X, y)\nprint(f\"\u56de\u5f52\u7cfb\u6570: {coefficients}\")\n\n# \u9884\u6d4b\u65b0\u6570\u636e\nX_new = np.array([[2, 3], [4, 5]])\npredictions = ols_predict(X,y, X_new)\nprint(f\"\u9884\u6d4b\u7ed3\u679c: {predictions}\")\n```\n\n#### 2.2 \u533a\u95f4\u7edf\u8ba1 (min_range_loop \u548c max_range_loop)\n\u8ba1\u7b97\u6ed1\u52a8\u7a97\u53e3\u5185\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002\n\n```python\nfrom rust_pyfunc import min_range_loop, max_range_loop\n\n# \u51c6\u5907\u6570\u636e\ndata = [1.0, 4.0, 2.0, 5.0, 3.0, 6.0, 2.0]\n\n# \u8ba1\u7b97\u6ed1\u52a8\u7a97\u53e3\u6700\u5c0f\u503c\nmin_values = min_range_loop(data)\nprint(f\"\u6ed1\u52a8\u7a97\u53e3\u6700\u5c0f\u503c: {min_values}\")\n\n# \u8ba1\u7b97\u6ed1\u52a8\u7a97\u53e3\u6700\u5927\u503c\nmax_values = max_range_loop(data)\nprint(f\"\u6ed1\u52a8\u7a97\u53e3\u6700\u5927\u503c: {max_values}\")\n```\n\n### 3. \u6587\u672c\u5206\u6790\n\n#### 3.1 \u53e5\u5b50\u5411\u91cf\u5316 (vectorize_sentences \u548c vectorize_sentences_list)\n\u5c06\u53e5\u5b50\u8f6c\u6362\u4e3a\u8bcd\u9891\u5411\u91cf\u3002\n\n```python\nfrom rust_pyfunc import vectorize_sentences, vectorize_sentences_list\n\n# \u4e24\u4e2a\u53e5\u5b50\u7684\u5411\u91cf\u5316\ns1 = \"The quick brown fox\"\ns2 = \"The lazy brown dog\"\nv1, v2 = vectorize_sentences(s1, s2)\nprint(f\"\u53e5\u5b501\u7684\u8bcd\u9891\u5411\u91cf: {v1}\")\nprint(f\"\u53e5\u5b502\u7684\u8bcd\u9891\u5411\u91cf: {v2}\")\n\n# \u591a\u4e2a\u53e5\u5b50\u7684\u5411\u91cf\u5316\nsentences = [\n \"The quick brown fox\",\n \"The lazy brown dog\",\n \"A quick brown fox jumps\"\n]\nvectors = vectorize_sentences_list(sentences)\nfor i, vec in enumerate(vectors):\n print(f\"\u53e5\u5b50{i+1}\u7684\u8bcd\u9891\u5411\u91cf: {vec}\")\n```\n\n#### 3.2 Jaccard\u76f8\u4f3c\u5ea6 (jaccard_similarity)\n\u8ba1\u7b97\u4e24\u4e2a\u53e5\u5b50\u4e4b\u95f4\u7684Jaccard\u76f8\u4f3c\u5ea6\u3002\n\n```python\nfrom rust_pyfunc import jaccard_similarity\n\n# \u6d4b\u8bd5\u5b8c\u5168\u76f8\u540c\u7684\u53e5\u5b50\ns1 = \"The quick brown fox\"\ns2 = \"The quick brown fox\"\nsim1 = jaccard_similarity(s1, s2)\nprint(f\"\u5b8c\u5168\u76f8\u540c\u7684\u53e5\u5b50\u76f8\u4f3c\u5ea6: {sim1}\") # \u8f93\u51fa: 1.0\n\n# \u6d4b\u8bd5\u90e8\u5206\u76f8\u540c\u7684\u53e5\u5b50\ns3 = \"The lazy brown dog\"\nsim2 = jaccard_similarity(s1, s3)\nprint(f\"\u90e8\u5206\u76f8\u540c\u7684\u53e5\u5b50\u76f8\u4f3c\u5ea6: {sim2}\") # \u8f93\u51fa: 0.4\n\n# \u6d4b\u8bd5\u5b8c\u5168\u4e0d\u540c\u7684\u53e5\u5b50\ns4 = \"Hello world example\"\nsim3 = jaccard_similarity(s1, s4)\nprint(f\"\u5b8c\u5168\u4e0d\u540c\u7684\u53e5\u5b50\u76f8\u4f3c\u5ea6: {sim3}\") # \u8f93\u51fa: 0.0\n```\n\n### 4. \u5e8f\u5217\u5206\u6790\n\n#### 4.1 \u5206\u6bb5\u8bc6\u522b (identify_segments)\n\u8bc6\u522b\u5e8f\u5217\u4e2d\u7684\u8fde\u7eed\u5206\u6bb5\u3002\n\n```python\nfrom rust_pyfunc import identify_segments\n\n# \u51c6\u5907\u6570\u636e\ndata = [1, 1, 1, 2, 2, 3, 3, 3, 1, 1]\n\n# \u8bc6\u522b\u8fde\u7eed\u5206\u6bb5\nsegments = identify_segments(data)\nprint(f\"\u8fde\u7eed\u5206\u6bb5: {segments}\")\n# \u8f93\u51fa\u5f62\u5982: [(1, 3), (2, 2), (3, 3), (1, 2)]\n# \u8868\u793a\uff1a\u503c1\u8fde\u7eed\u51fa\u73b03\u6b21\uff0c\u503c2\u8fde\u7eed\u51fa\u73b02\u6b21\uff0c\u503c3\u8fde\u7eed\u51fa\u73b03\u6b21\uff0c\u503c1\u8fde\u7eed\u51fa\u73b02\u6b21\n```\n\n#### 4.2 \u6700\u5927\u8303\u56f4\u4e58\u79ef (find_max_range_product)\n\u5bfb\u627e\u5e8f\u5217\u4e2d\u4e58\u79ef\u6700\u5927\u7684\u533a\u95f4\u3002\n\n```python\nfrom rust_pyfunc import find_max_range_product\n\n# \u51c6\u5907\u6570\u636e\ndata = [2.0, -3.0, 4.0, -1.0, 2.0, 1.0, -5.0, 4.0]\n\n# \u67e5\u627e\u6700\u5927\u4e58\u79ef\u533a\u95f4\nstart_idx, end_idx, max_product = find_max_range_product(np.array(data,dtype=float))\nprint(f\"\u6700\u5927\u4e58\u79ef\u533a\u95f4: [{start_idx}, {end_idx}]\")\nprint(f\"\u6700\u5927\u4e58\u79ef\u503c: {max_product}\")\n```\n\n## \u6ce8\u610f\u4e8b\u9879\n\n1. \u6240\u6709\u51fd\u6570\u90fd\u7ecf\u8fc7Rust\u4f18\u5316\uff0c\u76f8\u6bd4Python\u539f\u751f\u5b9e\u73b0\u6709\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\n2. \u8f93\u5165\u6570\u636e\u9700\u8981\u7b26\u5408\u51fd\u6570\u8981\u6c42\u7684\u683c\u5f0f\u548c\u7c7b\u578b\n3. \u90e8\u5206\u51fd\u6570\uff08\u5982`transfer_entropy`\uff09\u7684\u53c2\u6570\u9700\u8981\u6839\u636e\u5177\u4f53\u573a\u666f\u8c03\u6574\u4ee5\u83b7\u5f97\u6700\u4f73\u7ed3\u679c\n4. \u6587\u672c\u5904\u7406\u51fd\u6570\u4f1a\u81ea\u52a8\u8fdb\u884c\u5927\u5c0f\u5199\u8f6c\u6362\u548c\u6807\u70b9\u7b26\u53f7\u5904\u7406\n\n## \u6027\u80fd\u5efa\u8bae\n\n1. \u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\uff0c\u4f18\u5148\u4f7f\u7528\u5e26\u6709\"fast\"\u540e\u7f00\u7684\u51fd\u6570\u7248\u672c\n2. \u6587\u672c\u5904\u7406\u65f6\uff0c\u5efa\u8bae\u9884\u5148\u8fdb\u884c\u6570\u636e\u6e05\u6d17\n3. \u65f6\u95f4\u5e8f\u5217\u5206\u6790\u65f6\uff0c\u6ce8\u610f\u6570\u636e\u7684\u9884\u5904\u7406\uff08\u5982\u5f52\u4e00\u5316\uff09\u53ef\u80fd\u4f1a\u5f71\u54cd\u7ed3\u679c\n\n## \u8d21\u732e\u6307\u5357\n\n\u6b22\u8fce\u63d0\u4ea4Issue\u548cPull Request\u6765\u6539\u8fdb\u8fd9\u4e2a\u9879\u76ee\u3002\u5728\u63d0\u4ea4\u4ee3\u7801\u524d\uff0c\u8bf7\u786e\u4fdd\uff1a\n\n1. \u4ee3\u7801\u7ecf\u8fc7\u5145\u5206\u6d4b\u8bd5\n2. \u6dfb\u52a0\u4e86\u9002\u5f53\u7684\u6587\u6863\u548c\u793a\u4f8b\n3. \u9075\u5faa\u9879\u76ee\u7684\u4ee3\u7801\u98ce\u683c\n\n## License\n\nMIT License\n\n",
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