ML
This module provides for the easiest way to implement Machine Learning algorithms. It also has inbuilt support for graphing and optimizers based in C.
Learn the module here:
This module uses a tensorflow backend.
Implemented Algorithms
 2D CNN
ml.cnn
 Basic MLP
ml.nn
 KMeans
ml.k_means
 Linear Regression
ml.linear_regression
 optimized with C
 Logistic Regression
ml.logistic_regression
 Graph Modules
ml.graph
 Graph any function with or without data points 
from ml.graph import graph_function, graph_function_and_data
 Graph any function with or without data points 
 Nonlinear Regression
ml.regression
 Optimizers 
ml.optimizer
optimized with C GradientDescentOptimizer 
from ml.optimizer import GradientDescentOptimizer
 GradientDescentOptimizer 

UNSTABLE  Character generating RNN 
ml.rnn
/examples
You can find examples for all of these in Pip installation
pip install mlpython
Python installation
git clone https://github.com/vivek3141/ml
cd ml
python setup.py install
Bash Installation
git clone https://github.com/vivek3141/ml
cd ml
sudo make install
Examples
Examples for all implemented structures can be found in /examples
.
In this example, linear regression is used.
First, import the required modules.
import numpy as np
from ml.linear_regression import LinearRegression
Then make the required object
l = LinearRegression()
This code below randomly generates 50 data points from 0 to 10 for us to run linear regression on.
# Randomly generating the data and converting the list to int
x = np.array(list(map(int, 10*np.random.random(50))))
y = np.array(list(map(int, 10*np.random.random(50))))
Lastly, train it. Set graph=True
to visualize the dataset and the model.
l.fit(data=x, labels=y, graph=True)
The full code can be found in /examples/linear_regression.py
Makefile
A Makefile is included for easy installation.
To install using make run
sudo make
Note: Superuser privileges are only required if python is installed at /usr/local/lib
License
All code is available under the MIT License
Contributing
Pull requests are always welcome, so feel free to create one. Please follow the pull request template, so your intention and additions are clear.
Contact
Feel free to contact me by:
 Email: vivnps.verma@gmail.com
 GitHub Issue: create issue