marcelcaraciolo

from numpy import loadtxt, where
from pylab import scatter, show, legend, xlabel, ylabel

#load the dataset
data = loadtxt('ex2data1.txt', delimiter=',')

X = data[:, 0:2]
y = data[:, 2]

pos = where(y == 1)

marcelcaraciolo

{% load thumbnails %}

({
  name:'{{ recommendation.name }}',
  profile_url: '{{ recommendation.get_absolute_url }}',
  follow_url: '{% url join_studygroup recommendation.slug %}',
  thumbnail: '{% get_thumbnail_url recommendation 'minimum' %}',
  type: '{{ type }}',
  itens_count: '{{ itens_count }}',
  why: { 

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace, mean, std, arange
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pylab import plot, show, xlabel, ylabel

#Evaluate the linear regression

def feature_normalize(X):
    '''
    Returns a normalized version of X where

marcelcaraciolo

def feature_normalize(X):
    '''
    Returns a normalized version of X where
    the mean value of each feature is 0 and the standard deviation
    is 1. This is often a good preprocessing step to do when
    working with learning algorithms.
    '''
    mean_r = []
    std_r = []

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace
from pylab import scatter, show, title, xlabel, ylabel, plot, contour


#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples

marcelcaraciolo

#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples
    m = y.size

    predictions = X.dot(theta).flatten()

marcelcaraciolo

X = data[:, 0]
y = data[:, 1]


#number of training samples
m = y.size

#Add a column of ones to X (interception data)
it = ones(shape=(m, 2))
it[:, 1] = X

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace
from pylab import scatter, show, title, xlabel, ylabel, plot, contour



#Load the dataset
data = loadtxt('ex1data1.txt', delimiter=',')

#Plot the data

marcelcaraciolo

import datetime
import sys
import random
cimport numpy as np




def _rank_dists(np.ndarray ranks1, np.ndarray ranks2):
    """Finds the difference between the values in ranks1 and ranks2 for keys

marcelcaraciolo

import datetime
import sys
import random
import numpy as np
from scipy.stats import spearmanr


def _rank_dists(ranks1, ranks2):
    """Finds the values in ranks1 and ranks2 for keys
    present in both arrays.