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import numpy as np

class Perceptron:
    
    def __init__(self,Weights,Biases):
        
        self.Weights = Weights
        self.Biases = Biases
        
    def Train(self, Training, LearningRate):

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#!/usr/bin/env python -tt

import numpy as np
import scipy as sp
import pylab as pl
from sklearn.feature_extraction.image import grid_to_graph
from sklearn.cluster import KMeans
from scikits.image.data import imread

# Read image

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#!/usr/bin/env python

from threading import Thread
from time import sleep
from traits.api import HasTraits, Instance, String, Float, Enum, Button
from traitsui.api import View, Item, Group, HSplit, Handler
from traitsui.menu import NoButtons
from scipy import rand, indices, exp, sqrt, sum
from chaco.api import ArrayPlotData, Plot
from enable.component_editor import ComponentEditor

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# You need to have matplotlib installed to be able to plot anything in Python.
# Suppose your data is a variable 'data'

from matplotlib.pyplot import plot, show
try:
    from numpy.fft import fft
except:
    from numpy import fft

x = data[:,1]  # 2nd column

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{"iso_language_code": "da", "to_user_name": "Jishnu", "to_user_id_str": "110358179", "profile_image_url_https": "https://si0.twimg.com/profile_images/1761677446/2850384465_604be38694_b__copy__normal.jpg", "from_user_id_str": "15649930", "text": "@jishnu7 Automated Data Analysis talk By S Anand is at A1 #PyconIndia2012", "from_user_name": "Ramakrishna Reddy", "in_reply_to_status_id_str": "252282073249902593", "profile_image_url": "http://a0.twimg.com/profile_images/1761677446/2850384465_604be38694_b__copy__normal.jpg", "id": 252284135371067392, "to_user": "jishnu7", "source": "<a href="http://twitter.com/">web</a>", "in_reply_to_status_id": 252282073249902593, "id_str": "252284135371067392", "from_user": "ramkrsna", "from_user_id": 15649930, "to_user_id": 110358179, "geo": null, "created_at": "Sun, 30 Sep 2012 05:49:59 +0000", "metadata": {"result_type": "recent"}}
{"iso_language_code": "da", "to_user_name": null, "to_user_id_str": "0", "profile_image_url_https": "https://si0.twimg.com/pr

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#!/usr/bin/env python

import os
import json
import numpy as np
from pandas import DataFrame, concat
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from matplotlib.pyplot import imshow, plot, show, figure, title, yticks

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Python modules required for tutorials at SciPy India 2013

Dear participants,

Here is a checklist of the packages that you will require in order to make the most out of the tutorials being offered at SciPy India 2013. For the list of tutorials offered, please visit http://scipy.in/2013/schedule/

1. NumPy
2. SciPy
3. Matplotlib

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\documentclass{beamer}
%
% Choose how your presentation looks.
%
% For more themes, color themes and font themes, see:
% http://deic.uab.es/~iblanes/beamer_gallery/index_by_theme.html
%
\mode<presentation>
{
  \usetheme{Warsaw}      % or try Darmstadt, Madrid, Warsaw, ...

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################################################################################
# Description:
# -------------
# This is a TraistUI (http://docs.enthought.com/traitsui) application that 
# is used for performing some exploratory data analysis on the training data
# from Kaggle's Galaxy Zoo competition. 
# (http://kaggle.com/c/galaxy-zoo-the-galaxy-challenge)
# Broadly, it can be thought of as a browser for the data, which shows the 
# training images, the target distribution functions and some metrics related 
# to the training data.

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console