yhaddad

import numpy as np
import tensorflow as tf

sess = tf.Session()

TYPE=np.float64

N = 1000000
data = np.random.normal(0, 1, N).astype(TYPE)

yhaddad

import numpy as np
import tensorflow as tf
from scipy.optimize import minimize
import matplotlib
matplotlib.use('PDF')
import matplotlib.pyplot as plt

# Generate dataset
data = np.random.normal(0, 1, 1000)

yhaddad

import scipy.stats
def clopper_pearson(k,n,alpha=0.32):
    """
    http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
    alpha confidence intervals for a binomial distribution of k expected successes on n trials
    Clopper Pearson intervals are a conservative estimate.
    """
    lo = scipy.stats.beta.ppf(alpha/2, k, n-k+1)
    hi = scipy.stats.beta.ppf(1 - alpha/2, k+1, n-k)
    return lo, hi

yhaddad

import requests
data = requests.get('http://api.wunderground.com/api/INSERT_KEY_HERE/geolookup/conditions/q/Switzerland/Zurich.json').json()
location = data['location']['city']
temp_c = data['current_observation']['temp_c']
print "Current temperature in %s is: %s C" % (location, temp_c)

yhaddad

"""Genetic Algorithmn Implementation
see:
http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php
"""
import random

class GeneticAlgorithm(object):
    def __init__(self, genetics):
        self.genetics = genetics
        pass

yhaddad

"""
Helper module for displaying ROOT canvases in ipython notebooks

Usage example:
    # Save this file as rootnotes.py to your working directory.
    
    import rootnotes
    c1 = rootnotes.default_canvas()
    fun1 = TF1( 'fun1', 'abs(sin(x)/x)', 0, 10)
    c1.SetGridx()