Hadi Sinaee
Computer Engineering Student
PythonBooks - Learn Python the easy way ! Scikit-learn. Python. Librairies/Frameworks/Langages. Programming. Python programming language. Libs. /sci/ guide. Reinforcement learning python. Machine Learning. Milk: Machine Learning Toolkit for Python. This is the code that I use for my research projects. Where can I get it?
Github as usual. Alternatively the python packages index also contains official releases,the latest of which can be obtained by: easy_install milk or: pip install milk if you use these tools. Examples Here is how to test how well you can classify some features,labels data, measured by cross-validation: import numpy as np import milk features = np.random.rand(100,10) # 2d array of features: 100 examples of 10 features each labels = np.zeros(100) features[50:] += .5 labels[50:] = 1 confusion_matrix, names = milk.nfoldcrossvalidation(features, labels) print 'Accuracy:', confusion_matrix.trace()/float(confusion_matrix.sum()) If want to use a classifier, you instanciate a learner object and call its train() method: Features Pythonic interface to libSVM. Article filed in categories: Software Work Python. PyML - machine learning in Python — PyML v0.7.3 documentation. Explore Python, machine learning, and the NLTK library. The challenge: Use machine learning to categorize RSS feeds I was recently given the assignment to create an RSS feed categorization subsystem for a client.
The goal was to read dozens or even hundreds of RSS feeds and automatically categorize their many articles into one of dozens of predefined subject areas. The content, navigation, and search functionality of the client website would be driven by the results of this daily automated feed retrieval and categorization.
The client suggested using machine learning, perhaps with Apache Mahout and Hadoop, as she had recently read articles about those technologies. Her development team and ours, however, are fluent in Ruby rather than Java™ technology. What is machine learning? My first question was, "what exactly is machine learning? " Classification. The Mahout and Ruby detours Armed with an understanding of what machine learning is, the next step was to determine how to implement it.
Finding Python and the NLTK Back to top pip Running programs. Machine Learning in Python Has Never Been Easier! At BigML we believe that over the next few years automated, data-driven decisions and data-driven applications are going to change the world. In fact, we think it will be the biggest shift in business efficiency since the dawn of the office calculator, when individuals had “Computer” listed as the title on their business card.
We want to help people rapidly and easily create predictive models using their datasets, no matter what size they are. Our easy-to-use, public API is a great step in that direction but a few bindings for popular languages is obviously a big bonus. Thus, we are very happy to announce an open source Python binding to BigML.io, the BigML REST API. You can find it and fork it at Github. The BigML Python module makes it extremely easy to programmatically manage BigML sources, datasets, models and predictions. Just like magic! We have tried to build a very simple binding just wrapping all the HTTP requests and responses to BigML.io within one class. Like this: Talk.dvi. Machine learning in Python — scikit-learn 0.13.1 documentation.
PyBrain. Maths. OpenCV. List of Articles in this Blog. OPENCV PROJECTS. Image processing - OpenCV detect numbers. Real-time object detection in OpenCV using SURF. Object detection (or rather, recognition) is one of the fundamental problems in computer vision and a lot of techniques have come up to solve it. Invariably all of them employ machine learning, because the computer has to first 'learn' that a particular bunch of pixels with particular properties is called a 'book', remember that information, and use it in future to say whether the query image has a book or not. You should know about two terms before reading on. Training images are the images which the detector uses to learn information. Query images are the images from which the detector, after learning, is supposed to detect object(s).
Generally, our aim in such experiments would be to achieve robust object recognition even when the object in a query image is at a different size or angle than the training images. These are called scale in-variance and rotation in-variance respectively, something like this - But, to quote Gandalf, oft hope shall be born when all is forlorn. Code. Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features) - Naotoshi Seo. Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features) Objective The OpenCV library provides us a greatly interesting demonstration for a face detection.
Furthermore, it provides us programs (or functions) that they used to train classifiers for their face detection system, called HaarTraining, so that we can create our own object classifiers using these functions. It is interesting. However, I could not follow how OpenCV developers performed the haartraining for their face detection system exactly because they did not provide us several information such as what images and parameters they used for training. My working environment is Visual Studio + cygwin on Windows XP, or on Linux. FYI: I recommend you to work haartrainig with something different concurrently because you have to wait so many days during training (it would possibly take one week).
A picture from the OpenCV website History Data Prepartion Positive (Face) Images. Creating a haar cascade classifier aka haar training. In the previous posts, I used haar cascade xml files for the detection of face, eyes etc.., In this post, I am going to show you, how to create your own haar cascade classifier xml files. It took me a total of 16 hours to do it. Hope you can do it even sooner, following this post Note : The below is only for linux opencv users. If you are a windows user, use this link For most of the dough, that is going to come, you will need these executable linux files. Here's the link for it. Before I start, remember two important definetions Positive images : These images contain the object to be detected Negative images : Absolutely except that image, anything can be present It's better to explain, with an example.
First of all, I took the photographs of three of my pens, along with some background, the pics looked like the one below I took a total of 7 photographs (I didn't care to count, for which pen, I took more photographs, out of three) with my 2MP camera phone and loaded them into my computer. . SURF in OpenCV « Achu's TechBlog. Let us now see what is SURF. SURF Keypoints of my palm SURF stands for Speeded Up Robust Features. It is an algorithm which extracts some unique keypoints and descriptors from an image. More details on the algorithm can be found here and a note on its implementation in OpenCV can be found here.
Co-ordinates from origin to the end of the image. Object detection using SURF is scale and rotation invariant which makes it very powerful. OpenCV library provides an example of detection called find_obj.cpp. The explanation of the code is straightforward. SURF keypoints of my mobile phone The above picture shows the SURF keypoints captured by myself holding a mobile phone. From the above set of images, it can be clearly seen that SURF keypoints are those pixels whose average intensity values differ much greatly from the immediate neighbors, and by using the descriptor, a relation between the reliable keypoints and the neighboring pixels are obtained. Like this: Like Loading... Linux OS.