ML-From-Scratch | Machine Learning From Scratch | Machine Learning library

by eriklindernoren Python Version: Current License: MIT

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kandi X-RAY | ML-From-Scratch Summary

ML-From-Scratch is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Example Codes applications. ML-From-Scratch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install ML-From-Scratch' or download it from GitHub, PyPI.

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

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ML-From-Scratch has a medium active ecosystem.
It has 20024 star(s) with 3880 fork(s). There are 972 watchers for this library.
It had no major release in the last 12 months.
There are 25 open issues and 36 have been closed. On average issues are closed in 38 days. There are 17 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of ML-From-Scratch is current.

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quality kandi Quality

ML-From-Scratch has 0 bugs and 0 code smells.

ML-From-Scratch Quality

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ML-From-Scratch Quality

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security Security

ML-From-Scratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
ML-From-Scratch code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

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ML-From-Scratch Security

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license License

ML-From-Scratch is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

ML-From-Scratch License

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ML-From-Scratch License

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ML-From-Scratch releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.

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Top functions reviewed by kandi - BETA

kandi has reviewed ML-From-Scratch and discovered the below as its top functions. This is intended to give you an instant insight into ML-From-Scratch implemented functionality, and help decide if they suit your requirements.

Train the discriminator

Generate images for the MNIST dataset
Set trainable value
Train the loss function

Calculate the cost for each sample

Compute the clusters for the given medoids
Calculates the cost of each sample
Compute the euclidean distance between two points

Backward of the layer

Build the discriminator layer

Predict cluster labels

Train the autoencoder

Find the most frequent items in the list

K - fold cross validation sets

Fit the network to the given data

Fit the model to the given data

Build the generator

Compute the DecisionStump

Fit the quadratic model

Train the model

Evolves the population

Run the crossover algorithm

Plots the class distribution in 2d

Evolve the population

Fit the model

Compute the posterior distribution

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ML-From-Scratch Key Features

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

ML-From-Scratch Examples and Code Snippets

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Installation

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$ git clone https://github.com/eriklindernoren/ML-From-Scratch
$ cd ML-From-Scratch
$ python setup.py install

Polynomial Regression

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$ python mlfromscratch/examples/polynomial_regression.py

Classification With CNN

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$ python mlfromscratch/examples/convolutional_neural_network.py

+---------+
| ConvNet |
+---------+
Input Shape: (1, 8, 8)
+----------------------+------------+--------------+
| Layer Type           | Parameters | Output Shape |
+----------------------+------------+--------------+
| Conv2D               | 160        | (16, 8, 8)   |
| Activation (ReLU)    | 0          | (16, 8, 8)   |
| Dropout              | 0          | (16, 8, 8)   |
| BatchNormalization   | 2048       | (16, 8, 8)   |
| Conv2D               | 4640       | (32, 8, 8)   |
| Activation (ReLU)    | 0          | (32, 8, 8)   |
| Dropout              | 0          | (32, 8, 8)   |
| BatchNormalization   | 4096       | (32, 8, 8)   |
| Flatten              | 0          | (2048,)      |
| Dense                | 524544     | (256,)       |
| Activation (ReLU)    | 0          | (256,)       |
| Dropout              | 0          | (256,)       |
| BatchNormalization   | 512        | (256,)       |
| Dense                | 2570       | (10,)        |
| Activation (Softmax) | 0          | (10,)        |
+----------------------+------------+--------------+
Total Parameters: 538570

Training: 100% [------------------------------------------------------------------------] Time: 0:01:55
Accuracy: 0.987465181058

Density-Based Clustering

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$ python mlfromscratch/examples/dbscan.py

Generating Handwritten Digits

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$ python mlfromscratch/unsupervised_learning/generative_adversarial_network.py

+-----------+
| Generator |
+-----------+
Input Shape: (100,)
+------------------------+------------+--------------+
| Layer Type             | Parameters | Output Shape |
+------------------------+------------+--------------+
| Dense                  | 25856      | (256,)       |
| Activation (LeakyReLU) | 0          | (256,)       |
| BatchNormalization     | 512        | (256,)       |
| Dense                  | 131584     | (512,)       |
| Activation (LeakyReLU) | 0          | (512,)       |
| BatchNormalization     | 1024       | (512,)       |
| Dense                  | 525312     | (1024,)      |
| Activation (LeakyReLU) | 0          | (1024,)      |
| BatchNormalization     | 2048       | (1024,)      |
| Dense                  | 803600     | (784,)       |
| Activation (TanH)      | 0          | (784,)       |
+------------------------+------------+--------------+
Total Parameters: 1489936

+---------------+
| Discriminator |
+---------------+
Input Shape: (784,)
+------------------------+------------+--------------+
| Layer Type             | Parameters | Output Shape |
+------------------------+------------+--------------+
| Dense                  | 401920     | (512,)       |
| Activation (LeakyReLU) | 0          | (512,)       |
| Dropout                | 0          | (512,)       |
| Dense                  | 131328     | (256,)       |
| Activation (LeakyReLU) | 0          | (256,)       |
| Dropout                | 0          | (256,)       |
| Dense                  | 514        | (2,)         |
| Activation (Softmax)   | 0          | (2,)         |
+------------------------+------------+--------------+
Total Parameters: 533762

Deep Reinforcement Learning

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$ python mlfromscratch/examples/deep_q_network.py

+----------------+
| Deep Q-Network |
+----------------+
Input Shape: (4,)
+-------------------+------------+--------------+
| Layer Type        | Parameters | Output Shape |
+-------------------+------------+--------------+
| Dense             | 320        | (64,)        |
| Activation (ReLU) | 0          | (64,)        |
| Dense             | 130        | (2,)         |
+-------------------+------------+--------------+
Total Parameters: 450

Image Reconstruction With RBM

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$ python mlfromscratch/examples/restricted_boltzmann_machine.py

Evolutionary Evolved Neural Network

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$ python mlfromscratch/examples/neuroevolution.py

+---------------+
| Model Summary |
+---------------+
Input Shape: (64,)
+----------------------+------------+--------------+
| Layer Type           | Parameters | Output Shape |
+----------------------+------------+--------------+
| Dense                | 1040       | (16,)        |
| Activation (ReLU)    | 0          | (16,)        |
| Dense                | 170        | (10,)        |
| Activation (Softmax) | 0          | (10,)        |
+----------------------+------------+--------------+
Total Parameters: 1210

Population Size: 100
Generations: 3000
Mutation Rate: 0.01

[0 Best Individual - Fitness: 3.08301, Accuracy: 10.5%]
[1 Best Individual - Fitness: 3.08746, Accuracy: 12.0%]
...
[2999 Best Individual - Fitness: 94.08513, Accuracy: 98.5%]
Test set accuracy: 96.7%

Genetic Algorithm

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$ python mlfromscratch/examples/genetic_algorithm.py

+--------+
|   GA   |
+--------+
Description: Implementation of a Genetic Algorithm which aims to produce
the user specified target string. This implementation calculates each
candidate's fitness based on the alphabetical distance between the candidate
and the target. A candidate is selected as a parent with probabilities proportional
to the candidate's fitness. Reproduction is implemented as a single-point
crossover between pairs of parents. Mutation is done by randomly assigning
new characters with uniform probability.

Parameters
----------
Target String: 'Genetic Algorithm'
Population Size: 100
Mutation Rate: 0.05

[0 Closest Candidate: 'CJqlJguPlqzvpoJmb', Fitness: 0.00]
[1 Closest Candidate: 'MCxZxdr nlfiwwGEk', Fitness: 0.01]
[2 Closest Candidate: 'MCxZxdm nlfiwwGcx', Fitness: 0.01]
[3 Closest Candidate: 'SmdsAklMHn kBIwKn', Fitness: 0.01]
[4 Closest Candidate: '  lotneaJOasWfu Z', Fitness: 0.01]
...
[292 Closest Candidate: 'GeneticaAlgorithm', Fitness: 1.00]
[293 Closest Candidate: 'GeneticaAlgorithm', Fitness: 1.00]
[294 Answer: 'Genetic Algorithm']

Association Analysis

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$ python mlfromscratch/examples/apriori.py
+-------------+
|   Apriori   |
+-------------+
Minimum Support: 0.25
Minimum Confidence: 0.8
Transactions:
    [1, 2, 3, 4]
    [1, 2, 4]
    [1, 2]
    [2, 3, 4]
    [2, 3]
    [3, 4]
    [2, 4]
Frequent Itemsets:
    [1, 2, 3, 4, [1, 2], [1, 4], [2, 3], [2, 4], [3, 4], [1, 2, 4], [2, 3, 4]]
Rules:
    1 -&gt; 2 (support: 0.43, confidence: 1.0)
    4 -&gt; 2 (support: 0.57, confidence: 0.8)
    [1, 4] -&gt; 2 (support: 0.29, confidence: 1.0)

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Community Discussions

Trending Discussions on ML-From-Scratch

Why the predicted value by LinearRegression is exactly the same as the true value?

Trending Discussions on ML-From-Scratch

QUESTION

Why the predicted value by LinearRegression is exactly the same as the true value?

Asked 2020-Sep-24 at 23:10

I'm doing a regression by LinearRegression and get the mean squared error 0. I think there should be some deviation(at least small). Could you please explain this phenomenon?

## Import packages
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import urllib.request

## Import dataset
urllib.request.urlretrieve('https://raw.githubusercontent.com/Data-Science-FMI/ml-from-scratch-2019/master/data/house_prices_train.csv',
                           'house_prices_train.csv')
df_train = pd.read_csv('house_prices_train.csv')
x = df_train['GrLivArea'].values.reshape(1, -1)
y = df_train['SalePrice'].values.reshape(1, -1)
print('The explanatory variable is', x)
print('The variable to be predicted is', y)

## Regression
reg = LinearRegression().fit(x, y)
mean_squared_error(y, reg.predict(x))
print('The MSE is', mean_squared_error(y, reg.predict(x)))
print('Predicted value is', reg.predict(x))
print('True value is', y)

The result is

The explanatory variable is [[1710 1262 1786 ... 2340 1078 1256]]
The variable to be predicted is [[208500 181500 223500 ... 266500 142125 147500]]
The MSE is 0.0
Predicted value is [[208500. 181500. 223500. ... 266500. 142125. 147500.]]
True value is [[208500 181500 223500 ... 266500 142125 147500]]

ANSWER

Answered 2020-Sep-24 at 23:10

While the comments are certainly correct that a model's score on its own training set will be inflated, it is unlikely to get a perfect fit with linear regression, especially with just one feature.

Your problem is that you've reshaped the data incorrectly: reshape(1, -1) makes an array of shape (1, n), so your model thinks it has n features and n outputs with only a single sample, and so is a multiple linear regression with a perfect fit. Try instead with reshape(-1, 1) for x and no reshaping for y.

Source https://stackoverflow.com/questions/64042359

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ML-From-Scratch

You can install using 'pip install ML-From-Scratch' or download it from GitHub, PyPI.
You can use ML-From-Scratch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.