ravi07bec

from numpy import array
from numpy import asarray
from numpy import zeros
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Embedding
# define documents

ravi07bec

#Term Frequency
def termfreq(document, word):
    N = len(document)
    occurance = len([token for token in document if token == word])
    return occurance/N

#Inverse Document Frequency
 
def inverse_doc_freq(word):
    try:

ravi07bec

from math import exp
from random import seed
from random import random

# Initialize a network
def initialize_network(n_inputs, n_hidden, n_outputs):
    network = list()
    hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)]
    network.append(hidden_layer)
    output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)]

ravi07bec

# Make a prediction with weights
def predict(row, weights):
    activation = weights[0]
    for i in range(len(row)-1):
        activation += weights[i + 1] * row[i]
    return 1.0 if activation >= 0.0 else 0.0

# Estimate Perceptron weights using stochastic gradient descent
def train_weights(train, l_rate, n_epoch):

ravi07bec

# Example of making predictions
from math import sqrt

# calculate the Euclidean distance between two vectors
def euclidean_distance(row1, row2):
    distance = 0.0
    for i in range(len(row1)-1):
        distance += (row1[i] - row2[i])**2
    return sqrt(distance)

ravi07bec

# Make a prediction with coefficients
def predict(row, coefficients):
    yhat = coefficients[0]
    for i in range(len(row)-1):
        yhat += coefficients[i + 1] * row[i]
    return yhat

# Estimate linear regression coefficients using stochastic gradient descent
def coefficients_sgd(train, l_rate, n_epoch):
    coef = [0.0 for i in range(len(train[0]))]

ravi07bec

artist
 jr.
 stills & nash
 the creator
 wind & fire
!!!
10cc
112
12 stones
2 chainz

ravi07bec

0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99
0.3477159	-0.10670821	0.45994064	-0.34369546	-0.122856915	-0.7744754	0.19030279	0.05720891	0.1698621	-0.169827	-0.40953735	-0.23523934	0.048396867	0.7052571	0.34191108	-0.049767286	0.21731612	-0.64538217	-0.23329686	0.34425774	0.051952425	-0.4196746	-0.17587171	0.55343366	0.35680598	-0.16747452	-0.87103546	-0.030037874	-0.7526807	0.40080637	-0.22336994	0.021601401	-0.20519453	-0.60855657	0.32687736	0.27665842	-0.16321175	-0.5943156	0.31605804	-0.4849278	-0.039805546	-0.5149964	0.5394806	0.12340542	-0.29706863	-0.21343571	-0.17491385	0.25965297	-0.061519373	-0.1642966	-0.53483236	-0.8100034	0.01949798	0.11995843	0.003958153	-0.27783373	0.13840675	-0.23143578	0.3111198	-0.19826624	-0.05331649	0.42159384	0.04027778	0.24033114	0

ravi07bec

joe echo
crowded house
paul mccartney
joshua radin
the len price 3
noah and the whale
pearl jam
bruce springsteen
miles kane
tom petty

ravi07bec

Time Commitment: 50 hours, Nov 23- Nov 30 ‘2018
Create baseline model with python:
* Graph theory with python
* Poster representation
* Search-Even Misspelled words
* Multiple movies attributes- Persona and Word Cloud
* Filter of Genre, IMDB rating, Popularity and actor
* Comments and Screenshot capability- Login
* Hosting with a Virtual Machine