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Algorithm Libraries

Algorithm libraries provide easy access to a wide variety of mathematics, data science, and engineering functions.

Python

Python built-in libraries include:

• Python 2 Built-in Functions

• Python 3 Built-in Functions

SciKit Learn

SciKit Learn is s library for Machine Learning built on Python, NumPy, and Matplotlib. It includes:

• Clustering

• Cross Decomposition

• Decision Trees

• Decomposing Signals in Components

• Ensemble Models

• Gaussian Processes

• Generalized Linear Models

• Isotonic Regression

• Kernel Ridge Regression

• Linear and Quadratic Discriminant Analysis

• Model Selection and Analysis

• Multiclass and Multilabel Algorithms

• Naive Bayes

• Nearest Neighbors

• Neural Network Models

• Preprocessing Data

• Probability Calibration

• Semi-Supervised

• Stochastic Gradient Descent

• Support Vector Machines

A User Guide can be found here.

SciPy

SciPy is Python-based open-source software for mathematics, science, and engineering. SciPy includes:

• SciPy Library - Library for scientific computing

• NumPy - N-dimensional array package

• Matplotlib - 2D plotting

• IPython - Interactive console

• Pandas - Data structures and analysis

• Sympy - Symbolic mathematics

To install the SciPy stack, use the instruction here.

SciPy has a large number of functions organized into API packages:

• Clustering package (scipy.cluster)

• Compressed Sparse Graph Routines (scipy.sparse.csgraph)

• Constants (scipy.constants)

• Discrete Fourier transforms (scipy.fftpack)

• Integration and ODEs (scipy.integrate)

• Interpolation (scipy.interpolate)

• Input and output (scipy.io)

• Linear algebra (scipy.linalg)

• Low-level callback functions

• Miscellaneous routines (scipy.misc)

• Multi-dimensional image processing (scipy.ndimage)

• Orthogonal distance regression (scipy.odr)

• Optimization and root finding (scipy.optimize)

• Signal processing (scipy.signal)

• Sparse matrices (scipy.sparse)

• Sparse linear algebra (scipy.sparse.linalg)

• Spatial algorithms and data structures (scipy.spatial)

• Special functions (scipy.special)

• Statistical functions (scipy.stats)

• Statistical functions for masked arrays (scipy.stats.mstats)

A more detailed index of all SciPy functions can be found here.

Statsmodels

Statsmodels is a set of Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration, including:

• Analysis of Variance (ANOVA)

• Contingency

• Datasets Package

• Distributions

• Empirical Likelihood

• Generalized Linear Models

• Generalized Estimating Equations

• Graphics

• Input-Output

• Linear Mixed Effects Models

• Linear Regression

• Method of Moments

• Multiple Imputation with Chained Equations

• Multivariate Statistics

• Nonparametric Methods

• Regression with Discrete Dependent Variable

• Robust Linear Models

• Sandbox

• Statistics

• Survival and Duration Analysis

• Time Series analysis

• Time Series Analysis by State Space Methods

• Tools

Datasets

Dataset loading utilities are useful for testing and learning about Machine Learning algorithms. Datasets include:

• Toy Datasets

• Sample Images

• Sample Generators

• Loading External Datasets

• News Text

• Labeled Faces

• Disease Diagnostic Data

• Handwritten Digits

• Iris Plants Data