margrami

import json
import sqlite3


# Make some fresh tables using executescript()
conn = sqlite3.connect('booksRDBMS.sqlite', timeout=10)
cur = conn.cursor()


cur.executescript('''

margrami

# Creating a database table  from a cvs file
import csv, sqlite3
conn = sqlite3.connect('wrangling/data_wrangling.sqlite')
cur = conn.cursor()
cur.executescript('''

DROP TABLE IF EXISTS view_item_event;

CREATE TABLE view_item_event(
    event_id VARCHAR(32) NOT NULL PRIMARY KEY,

margrami

# old fashion way:
listA = [1, 2, 3, 4]
squares = []
for i in listA:
    square.append(i**2)
   
# Ahora con LofC
    
square = [i**2 for i in listA]

margrami

from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten
import numpy as np
import matplotlib.pyplot as plt
from cnn_utils import *
from scipy import ndimage
import math
from mreDeepLTools import *

# Loading the data (signs)

margrami

from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten
import numpy as np
import matplotlib.pyplot as plt
from cnn_utils import *
from scipy import ndimage
import math
from mreDeepLTools import *

# Loading the data (signs)

margrami

# Print the only the lastname 

people = ['Dr. Christopher Brooks', 'Dr. Kevyn Collins-Thompson', 
          'Dr. VG Vinod Vydiswaran', 'Dr. Daniel Romero']
for person in people:
    g = (lambda x: x.split()[0] + x.split()[-1])
    print g(person)
# result: 
# Dr.Brooks
# Dr.Collins-Thompson

margrami

import re
# The simplest use of the regular expression library is the search() function.

hand = open('mbox-short.txt')
for line in hand:
    line = line.rstrip()
    if re.search('From:', line) :
        print line

# Handling The Data

margrami

def mapFeature(x1, x2):
    '''
    Maps the two input features to quadratic features.
    Returns a new feature array with more features, comprising of
    X1, X2, X1 ** 2, X2 ** 2, X1*X2, X1*X2 ** 2, etc...
    Inputs X1, X2 must be the same size
    '''
    x1.shape = (x1.size, 1)
    x2.shape = (x2.size, 1)
    degree = 6

margrami

training_error = sum(yr != y)/float(m)
print training_error

margrami

# numero de m - filas, n numero de colunmas 
m , n = X.shape
#print m, n

def sigmoid(x):
    return 1 /(1 + np.exp(-x))

# Esta funcion de costo esta perfecta y los vectores 
# entran directamente. 
# No hay necesidad de vector colunma para theta