| Name | latteOne JSON |
| Version |
0.0.5
JSON |
| download |
| home_page | |
| Summary | simple classification algorithms |
| upload_time | 2023-07-18 12:40:41 |
| maintainer | |
| docs_url | None |
| author | Sebinmon Vr |
| requires_python | |
| license | MIT |
| keywords |
latte_classifier
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
#Latte
This is a simple package for data classification
-------------------------------------------------------------------------------------------
#Contents/functions
-------------------------------------------------------------------------------------------
# Linear Regression
The `linear(filename, cl1, cl2, predict_value)` function performs linear regression on a given dataset and predicts the value for a given input. It takes the following parameters:
- `filename`: The name of the CSV file containing the dataset.
- `cl1`: The column index of the independent variable (x).
- `cl2`: The column index of the dependent variable (y).
- `predict_value`: The input value for which the prediction is to be made.
The function calculates the slope and intercept of the regression line using the least squares method. It then plots the data points, regression line, and prediction. The function returns the slope, intercept, and prediction as results.
# Logistic Regression
The `logistic(filename, cl1, cl2, predict_value)` function performs logistic regression on a given dataset and predicts the probability and class for a given input. It takes the following parameters:
- `filename`: The name of the CSV file containing the dataset.
- `cl1`: The column index of the independent variable (x).
- `cl2`: The column index of the dependent variable (y).
- `predict_value`: The input value for which the prediction is to be made.
The function uses scikit-learn's `LogisticRegression` class to fit the logistic regression model and predict the probability and class for the input. It plots the data points, logistic regression curve, and prediction. The function returns the probability and prediction as results.
# Decision Tree Classification
The `dtree(filename, cl1, cl2, predict_value)` function performs decision tree classification on a given dataset and predicts the class for a given input. It takes the following parameters:
- `filename`: The name of the CSV file containing the dataset.
- `cl1`: The column index of the independent variable (x).
- `cl2`: The column index of the dependent variable (y).
- `predict_value`: The input value for which the prediction is to be made.
The function uses scikit-learn's `DecisionTreeClassifier` class to fit the decision tree model and predict the class for the input. It plots the data points, decision tree boundary, and prediction. The function returns the prediction as a result.
# Matrix Operations
The `matrix_ml(matrix1, matrix2)` function performs matrix multiplication on two given matrices. It takes the following parameters:
- `matrix1`: The first matrix.
- `matrix2`: The second matrix.
The function uses numpy's `dot` function to perform matrix multiplication and returns the result.
The `matrix_add(matrix1, matrix2)` function performs matrix addition on two given matrices. It takes the following parameters:
- `matrix1`: The first matrix.
- `matrix2`: The second matrix.
The function uses numpy's `add` function to perform matrix addition and returns the result.
Change Log
==========
0.0.5 (18/07/2023)
-------------------
Raw data
{
"_id": null,
"home_page": "",
"name": "latteOne",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "latte_classifier",
"author": "Sebinmon Vr",
"author_email": "nthn8777@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/dc/1d/f77fbb6ed7c724d7f0ab7939a004585a96891a52c08168f2591a27d3c614/latteOne-0.0.5.tar.gz",
"platform": null,
"description": " #Latte\r\n\r\nThis is a simple package for data classification \r\n\r\n-------------------------------------------------------------------------------------------\r\n#Contents/functions\r\n-------------------------------------------------------------------------------------------\r\n# Linear Regression\r\n\r\nThe `linear(filename, cl1, cl2, predict_value)` function performs linear regression on a given dataset and predicts the value for a given input. It takes the following parameters:\r\n- `filename`: The name of the CSV file containing the dataset.\r\n- `cl1`: The column index of the independent variable (x).\r\n- `cl2`: The column index of the dependent variable (y).\r\n- `predict_value`: The input value for which the prediction is to be made.\r\n\r\nThe function calculates the slope and intercept of the regression line using the least squares method. It then plots the data points, regression line, and prediction. The function returns the slope, intercept, and prediction as results.\r\n\r\n# Logistic Regression\r\n\r\nThe `logistic(filename, cl1, cl2, predict_value)` function performs logistic regression on a given dataset and predicts the probability and class for a given input. It takes the following parameters:\r\n- `filename`: The name of the CSV file containing the dataset.\r\n- `cl1`: The column index of the independent variable (x).\r\n- `cl2`: The column index of the dependent variable (y).\r\n- `predict_value`: The input value for which the prediction is to be made.\r\n\r\nThe function uses scikit-learn's `LogisticRegression` class to fit the logistic regression model and predict the probability and class for the input. It plots the data points, logistic regression curve, and prediction. The function returns the probability and prediction as results.\r\n\r\n# Decision Tree Classification\r\n\r\nThe `dtree(filename, cl1, cl2, predict_value)` function performs decision tree classification on a given dataset and predicts the class for a given input. It takes the following parameters:\r\n- `filename`: The name of the CSV file containing the dataset.\r\n- `cl1`: The column index of the independent variable (x).\r\n- `cl2`: The column index of the dependent variable (y).\r\n- `predict_value`: The input value for which the prediction is to be made.\r\n\r\nThe function uses scikit-learn's `DecisionTreeClassifier` class to fit the decision tree model and predict the class for the input. It plots the data points, decision tree boundary, and prediction. The function returns the prediction as a result.\r\n\r\n# Matrix Operations\r\n\r\nThe `matrix_ml(matrix1, matrix2)` function performs matrix multiplication on two given matrices. It takes the following parameters:\r\n- `matrix1`: The first matrix.\r\n- `matrix2`: The second matrix.\r\n\r\nThe function uses numpy's `dot` function to perform matrix multiplication and returns the result.\r\n\r\nThe `matrix_add(matrix1, matrix2)` function performs matrix addition on two given matrices. It takes the following parameters:\r\n- `matrix1`: The first matrix.\r\n- `matrix2`: The second matrix.\r\n\r\nThe function uses numpy's `add` function to perform matrix addition and returns the result.\r\n\r\n\r\n\r\n\r\nChange Log\r\n==========\r\n\r\n0.0.5 (18/07/2023)\r\n-------------------\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "simple classification algorithms",
"version": "0.0.5",
"project_urls": null,
"split_keywords": [
"latte_classifier"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4e0b4fcbf27bd5cfd672b81fcb5ae49602189e41359490f97fe3b0075db826a6",
"md5": "c3aac31ee756c8cd5659189720106d1b",
"sha256": "3fb0a80da3ab8374400c939bd10125c7e38d5c5cdc616d2ba45a17caae19b3cf"
},
"downloads": -1,
"filename": "latteOne-0.0.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c3aac31ee756c8cd5659189720106d1b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 2730,
"upload_time": "2023-07-18T12:40:39",
"upload_time_iso_8601": "2023-07-18T12:40:39.954375Z",
"url": "https://files.pythonhosted.org/packages/4e/0b/4fcbf27bd5cfd672b81fcb5ae49602189e41359490f97fe3b0075db826a6/latteOne-0.0.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dc1df77fbb6ed7c724d7f0ab7939a004585a96891a52c08168f2591a27d3c614",
"md5": "2664cb58a8e2fcf44af35165a972f17b",
"sha256": "1a14eafcda3b2677fe9258378dbb13e205f52307a78fde0a225a4a3552e83fa0"
},
"downloads": -1,
"filename": "latteOne-0.0.5.tar.gz",
"has_sig": false,
"md5_digest": "2664cb58a8e2fcf44af35165a972f17b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 4588,
"upload_time": "2023-07-18T12:40:41",
"upload_time_iso_8601": "2023-07-18T12:40:41.784413Z",
"url": "https://files.pythonhosted.org/packages/dc/1d/f77fbb6ed7c724d7f0ab7939a004585a96891a52c08168f2591a27d3c614/latteOne-0.0.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-07-18 12:40:41",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "latteone"
}
