https://github.com/marcos-venicius/smlf
A small machine learning framework with ONLY python and math
https://github.com/marcos-venicius/smlf
artificial-intelligence deep-learning ia machine-learning ml models neural-network xor
Last synced: 7 months ago
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A small machine learning framework with ONLY python and math
- Host: GitHub
- URL: https://github.com/marcos-venicius/smlf
- Owner: marcos-venicius
- License: mit
- Created: 2023-12-12T15:14:19.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-13T22:39:09.000Z (almost 2 years ago)
- Last Synced: 2025-03-26T00:51:19.923Z (8 months ago)
- Topics: artificial-intelligence, deep-learning, ia, machine-learning, ml, models, neural-network, xor
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 13
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SMLF
**Small Machine Learning Framework**
This is a small framework to machine learning models.
We have a class `Matrix` and a class `NeuralNetwork`.
# Examples
## Xor
[view the file](./examples/xor.py)
```console
cd examples
python3 xor.py
```
Output example:
# Docs
## Matrix docs
If you wanna print the `Matrix`:
```py
m = Matrix(2, 2)
print(m)
```
If you wanna randomize the `Matrix`:
```py
rand(self) -> None
```
If you wanna fill the `Matrix` with just one value:
```py
fill(self, x: float) -> None
```
If you wanna initialize with specific values:
```py
m1 = Matrix(2, 2, [[0, 1], [0, 1]]) # <-- use the multidimensional array as the third parameter
```
If you wanna set a specific value to a specific location:
```py
set(self, row: int, col: int, x: float) -> None
```
If you wanna sum the `Matrix` with another:
```py
m1 = Matrix(2, 2)
m1.rand()
m2 = Matrix(2, 2)
m2.rand()
m3 = m1.sum(m2) # <-- do this
```
If you wanna apply the sigmoid activation function:
```py
apply_sigmoid(self) -> None
```
If you wanna get a row:
```py
row(row: int) -> Matrix
```
If you wanna get a value a specifc row and column:
```py
at(r: int, c: int) -> float
```
If you wanna multiply the `Matrix` by another:
```py
m1 = Matrix(2, 2)
m2 = Matrix(2, 2)
m1.rand()
m2.rand()
m3 = m1.dot(m2) # <-- do this
```
## NeuralNetwork docs
The neural network expects an architecture like `[2, 2, 1]`, that means:
* 2 inputs
* 1 hidden layer
* 1 output
If you wanna print the `NeuralNetwork`:
```py
nn = NeuralNetwork([2, 2, 1])
print(nn) # <-- do this
```
If you wanna get the `NeuralNetwork` input:
```py
input(self) -> Matrix
```
If you wanna get the `NeuralNetwork` output:
```py
output(self) -> Matrix
```
If you wanna set the input mannually:
```py
set_input(self, input: Matrix) -> None
```
If you wanna randomize the Neural Network (between 0 and 1):
```py
rand(self) -> None
```
If you wanna forward:
```py
forward(self) -> None
```
If you wanna calculate the cost:
```py
cost(self, train_input: Matrix, train_output: Matrix) -> float
```
If you wanna do a finite diff manually (it's made internally):
```py
finite_diff(self, g: NeuralNetwork, epsilon: float, train_input: Matrix, train_output: Matrix) -> None
```
If you wanna make the model learn manually (it's made internally):
```py
learn(self, g: NeuralNetwork, learning_rate: float) -> None
```
If you wanna train the model:
```py
train(self, g: NeuralNetwork, learning_rate: float, epsilon: float, train_input: Matrix, train_output: Matrix, epochs: int) -> None
```