NumPy

Python: An Emerging Tool in Computational Biology and Bioinformatics Xudong Luan Harrisburg University of Science and Technology Harrisburg, PA, USA Abstract—This research paper reviews journal articles in the domain of using Python for Computational Biology and Bioinformatics and demonstrates a Python project to mine any DNA sequence and display the results. Keywords—Computational Biology; Bioinformatics; Python; Biopython; Matplotlib; DNA Mining I. INTRODUCTION Biologists and life science

import numpy import matplotlib.pyplot as plt class Sphere: def __init__(self): z=1 def add(self, points, radius, color): return dict(type='sphere', points=numpy.array(points), radius=numpy.array(radius), color=numpy.array(color), reflection=.5) def intersect(self, e, s, pos, Rad): # Check for intersects A = numpy.dot(s, s) B = 2 * numpy.dot(s, (e - pos)) C = numpy.dot((e - pos), (e - pos)) - Rad * Rad delta = B *

''' @summary: To remove keywords that are similar. This is to bring more diversity to the top ranked keywords. We use an algorithm called K-means for grouping. This algorithm is implemented and have not used third party library other than numpy (for computation) and gensim (loading of data set) Each word in the keyword list is converted to a vector using word2vec. We use GoogleNews pre-trained dataset to get vector of word. Then, we divide the n keywords into k clusters. Initially, we randomly choose

cells with value difference greater than Set1 belong to the same class. Though the ground truth label in Set3 seems to be fine, the difference between Red highlighted cells seem to be almost the same as the class label discriminating cells. Figure 26: NumPy version of input and ground truth This closeness of values posed a great difficulty in training and necessitated a lot of ‘forgetting of memory’ in the network (by means of dropout) to achieve good results. PAGE 49 In addition, as seen in below figure

Parallel Support Vector Machine Junfeng Wu Junming Chen May 6, 2016 1 INTRODUCTION Support vector machines is a supervised machine learning alogrithom used for classification. The problem could be written : minimize 1 |w |2 2 yi((w,xi)+b)−1≥0 where w is a linear combination of the training data: n w = αi k(xi ) i=1 this could be further written in a dual form[5]: min 1αTQα−eTα α2 0≤αi ≤C, yTα=0, ∀i ≤n where Q is the kernel matrix. This dual form is a quadratic programming problem with linear

1. Please describe how you would structure your approach and the technology that would be required to acquire the data necessary to identify Amazon’s global carbon emissions. The Earth’s climate has been rapidly warming for the past several decades due to the burning of fossil fuels, with the subsequent release of carbon dioxide and other greenhouse gases (GHG). Global climate change has already caused glaciers and sea ice to melt, plant and animal ranges to shift, sea level rise to accelerate, oceans

important role in building the classifier and designing the machine learning algorithm. Scikit-learn \cite{scikit} is an open source library which provides efficient tools for data mining and data analysis. It is based on other Python libraries like NumPy \cite{Numpy}, Scipy \cite{Scipy} and Matplotlib \cite{Matplot}. Scikit \cite{scikit} provides various functions and methods for classification and hence helps to build an efficient classifier to detect anomalies in the data. \section{Machine learning algorithms}

results while training and testing. 4. Though the chosen deep learning framework – Tensorflow – is quite popular, it is still in its nascent stages and lacked a few features that made model training gruesome for some complex tasks. For e.g. lack of NumPy like indexing in Python, inadequate support for multi-label implementations etc. consumed a lot of time during model training to tide over the difficulties. 3. Model Implementation The U-net (Ronneberger et al., 2015) extends the typical sliding-window

Abstract— In this paper, an approach for gender classification is carried out combining frontal face images, Haar cascades, Histogram of Oriented Gradients and Support Vector Machines. The comparison of the existing methods that delves into the effects of Haar Cascade Classifier and Histogram of Oriented Gradients(HOG) for Face Detection and the use of Support Vector Machines(SVM) for Gender Classification. A database of 2-D facial images was used, consisting of individual as well as group photographs

had previously attend few workshops about Python program, but this is the first one that targeted specifically people working in astronomy. The organizers, which mainly consisted of astropy volunteers, started the workshop with an introduction about numpy and astropy. Astropy is a project of unifying all astro-related python packages into one package that