Self Organizing Maps
A collection of sloppy snippets for scientific computing and data visualization in Python. Notice: For an update tutorial on how to use minisom refere to the examples in the official documentation. The Self Organizing Maps (SOM), also known as Kohonen maps, are a type of Artificial Neural Networks able to convert complex, nonlinear statistical relationships between high-dimensional data items into
Combining Scikit-Learn and NTLK
A collection of sloppy snippets for scientific computing and data visualization in Python. In Chapter 6 of the book Natural Language Processing with Python there is a nice example where is showed how to train and test a Naive Bayes classifier that can identify the dialogue act types of instant messages. Th classifier is trained on the NPS Chat Corpus which consists of over 10,000 posts from instan
Hopalong fractals
Have you ever wondered what happen if you pick a point (x0,y0) and compute hundreds of point using these equations? Well, you get a hopalong fractal. Let's plot this fractal using Pylab. The following function computes n points using the equations above: from __future__ import division from numpy import sqrt,power def hopalong(x0,y0,n,a=-55,b=-1,c=-42): def update(x,y): x1 = y-x/abs(x)*sqrt(abs(b*
Betweenness Centrality
A collection of sloppy snippets for scientific computing and data visualization in Python. In Network Analysis the identification of important nodes is a common task. We have various centrality measures that we can use and in this post we will focus on the Betweenness Centrality. We will see how this measure is computed and how to use the library networkx in order to create a visualization of the
A toy Bloom Filter
A collection of sloppy snippets for scientific computing and data visualization in Python. A Bloom Filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set. It is based on a probabilistic mechanism where false positive retrieval results are possible, but false negatives are not. In this post we will see a pure python implementation of
First steps with networkx
A collection of sloppy snippets for scientific computing and data visualization in Python. One of my favorite topics is the study of structures and, inspired by the presentation of Jacqueline Kazil and Dana Bauer at PyCon US, I started to use networkx in order to analyze some networks. This library provides a lot facilities for the creation, the visualization and the mining of structured data. So,
Text to Speech with correct intonation
A collection of sloppy snippets for scientific computing and data visualization in Python. Google has an unoffciale text to speech API. It can be accessed by http requests but it is limited to strings with less than 100 characters. In this post we will see how to split a text longer than 100 characters in order to obtain a correct voice intonation with this service. The approach is straighforward,
Distribution fitting with scipy
A collection of sloppy snippets for scientific computing and data visualization in Python. Distribution fitting is the procedure of selecting a statistical distribution that best fits to a dataset generated by some random process. In this post we will see how to fit a distribution using the techniques implemented in the Scipy library. This is the first snippet: from scipy.stats import norm from nu
Convolution with numpy
A convolution is a way to combine two sequences, x and w, to get a third sequence, y, that is a filtered version of x. The convolution of the sample xt is computed as follows: It is the mean of the weighted summation over a window of length k and wt are the weights. Usually, the sequence w is generated using a window function. Numpy has a number of window functions already implemented: bartlett, b
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Shape Matching Experiments
A colorful Trifoil Knot
Binary Plots
Finite differences with Toeplitz matrix
Visualizing the tangent
Box-Muller Transformation
3D stem plot
Visualizing correlation matrices
Face and eyes detection in OpenCV