felipessalvatore

#--------TENSORFLOW-------------
#MULTINOMIAL LOGISTIC CLASSIFIER
#APPLIED TO THE IRIS DATASET

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import scipy as sp
from sklearn.datasets import load_iris
%matplotlib inline

felipessalvatore

import numpy as np
import scipy.spatial as ss


u = np.array([1,2,3])
u1 = np.array([1,2,3])
v = np.array([2,3,-4])

a = ss.distance.cosine(u, v)
a1 = ss.distance.cosine(u, u1)

felipessalvatore

import numpy as np
from numpy import linalg as la

a = np.array([[3,1,1],[-1,3,1]],dtype='float32')
b = np.matmul(a,a.T)
eivalues, eivectors =la.eig(b)

#CAREFUL : the match is between the list
#of eigenvectors.T and eigenvalues

felipessalvatore

import smtplib
from email.MIMEMultipart import MIMEMultipart
from email.MIMEText import MIMEText
from email.MIMEBase import MIMEBase
from email import encoders
 
fromaddr = "YOUR EMAIL"
toaddr = "EMAIL ADDRESS YOU SEND TO"
 
msg = MIMEMultipart()

felipessalvatore

x = float(input('Digite um número qualquer (positivo ou negativo): '))
y = str(input('Digite um substantivo precedido de seu artigo definido: '))
z = str(input('Digite um predicado que concorde em número e gênero com o substantivo: '))
if x > 0:
    w = "Na sociedade dominada pela indústria cultural, "
    k = "instante único"
    u = " em si"
    l = " Benjamin "
elif x < 0:

felipessalvatore

https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/

felipessalvatore

http://www.atmos.washington.edu/~dennis/MatrixCalculus.pdf

felipessalvatore

import tracemalloc

tracemalloc.start()

# ... run your application ...

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

print("[ Top 10 ]")

felipessalvatore

import numpy as np
import random
import unittest
from math import isnan


def softmax(x):
    """
    Compute the softmax function for each row of the input x.
    It is crucial that this function is optimized for speed because

felipessalvatore

#code adapted from http://tillbergmann.com/blog/python-gradient-descent.html

%matplotlib inline
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy import stats 

from sklearn.datasets.samples_generator import make_regression