├── FinalReport-SMAIProject.pdf ├── Movie Rating Prediction using RBM _ Final.pdf ├── Movie Rating Prediction using RBM _ Final.pptx ├── README.md ├── code ├── get_trust.py ├── data.py ├── data_handler.py ├── simple_rbm.py ├── model.py └── model2.py ├── .gitignore └── LICENSE /FinalReport-SMAIProject.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pinkeshbadjatiya/movie-reco-using-RBM/HEAD/FinalReport-SMAIProject.pdf -------------------------------------------------------------------------------- /Movie Rating Prediction using RBM _ Final.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pinkeshbadjatiya/movie-reco-using-RBM/HEAD/Movie Rating Prediction using RBM _ Final.pdf -------------------------------------------------------------------------------- /Movie Rating Prediction using RBM _ Final.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pinkeshbadjatiya/movie-reco-using-RBM/HEAD/Movie Rating Prediction using RBM _ Final.pptx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Restricted Boltzmann Machines for Collaborative Filtering 2 | 3 | 4 | **Implementation of http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf in python** 5 | 6 | - All the codes are in the folder ./code 7 | - model.py is the main program which runs the training and then predictor. 8 | - data.py is a module written to extract data from the Netflix Prize dataset. 9 | - simple_rbm.py is a simple implementation of a rbm for our model. 10 | 11 | - model2.py, data_handler.py, get_trust.py contains the code for our extended model. 12 | 13 | - Other files include the report and final presentation. 14 | 15 | - We did not include the dataset as it is 2GB in size. 16 | 17 | ### Directory Structure: 18 | . 19 | ├── code 20 | │   ├── data.py 21 | │   ├── model.py 22 | │   └── simple_rbm.py 23 | ├── FinalReport-SMAIProject.pdf 24 | ├── Movie Rating Prediction using RBM _ Final.pdf 25 | ├── Movie Rating Prediction using RBM _ Final.pptx 26 | └── README.md 27 | 28 | -------------------------------------------------------------------------------- /code/get_trust.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from os import listdir 3 | from os.path import isfile, join 4 | from data import get_data 5 | import numpy as np 6 | 7 | def trust(): 8 | 9 | print "getting trust" 10 | users = get_data() 11 | cutoff = 0.03 12 | 13 | trust_mat = [] 14 | 15 | print len(users.keys()) 16 | for user1 in users: 17 | for user2 in users: 18 | 19 | if user1 == user2: 20 | continue 21 | print user1, user2 22 | cnt = 0 23 | mo = {} 24 | 25 | for (m1,r1) in users[user1]: 26 | mo[m1] = 1 27 | for (m2,r2) in users[user2]: 28 | if mo.has_key(m2): 29 | cnt +=1 30 | 31 | if cnt/len(users[user1]) >= cutoff and cnt/len(users[user2])>=cutoff: 32 | trust_mat.append([user1, user2]) 33 | print "trust got" 34 | return users, np.asarray(trust_mat) 35 | 36 | 37 | if __name__ == "__main__": 38 | trust_mat = trust(users) 39 | 40 | 41 | import pdb 42 | pdb.set_trace() 43 | 44 | -------------------------------------------------------------------------------- /code/data.py: -------------------------------------------------------------------------------- 1 | from os import listdir 2 | from os.path import isfile, join 3 | 4 | 5 | mypath = "../data/training_set/" 6 | truncate = True 7 | def get_data(): 8 | files = [ f for f in listdir(mypath) if isfile(join(mypath, f)) ] 9 | 10 | users = {} 11 | 12 | ct = 0 13 | user_map = {} 14 | user_ct = 0 15 | for name in files: 16 | with open(mypath+''+name, 'r') as f: 17 | index = int(f.readline().split(':')[0]) 18 | 19 | for line in f.readlines(): 20 | [u, r] = line.split(',')[:2] 21 | u,r = int(u), int(r) 22 | if u not in user_map: 23 | user_map[u] = user_ct 24 | user_ct += 1 25 | u = user_map[u] 26 | if u in users: 27 | users[u].append((index, r)) 28 | else: 29 | users[u] = [] 30 | users[u].append((index, r)) 31 | ct += 1 32 | if ct%2: 33 | print ct 34 | 35 | temp = {} 36 | for i, user in enumerate(users): 37 | if truncate: 38 | if i >= 1000: 39 | break 40 | temp[user] = users[user] 41 | 42 | print "data got" 43 | return temp 44 | 45 | 46 | if __name__ == "__main__": 47 | users = get_data() 48 | import pdb 49 | pdb.set_trace() 50 | 51 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | -------------------------------------------------------------------------------- /code/data_handler.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | import numpy as np 4 | from get_trust import trust 5 | from scipy.io import loadmat 6 | import pdb 7 | 8 | class data_handler(): 9 | 10 | def __init__(self): 11 | self.n_users = 0 12 | self.n_prod = 0 13 | self.n_cat = 1 14 | 15 | def get_stats(self): 16 | return self.n_users, self.n_prod, self.n_cat 17 | 18 | def load_matrices(self): 19 | # Loading Matrices from data 20 | users, W = trust() 21 | # Converting R and W from dictionary to array 22 | R = [] 23 | for user in users: 24 | for (m, r) in users[user]: 25 | R.append([user, m, r, 0]) 26 | R = np.asarray(R) 27 | pdb.set_trace() 28 | self.n_users = max(R[:, 0]) 29 | self.n_prod = max(R[:, 1]) 30 | # Selecting entries with the 6 categories given in the paper 31 | cat_id = [0] 32 | R_size = R.shape[0] 33 | # Choosing 70% data for training and rest for testing 34 | R_train = R[:R_size*0.7] 35 | R_test = R[R_size*0.7:] 36 | # Making all eligible Product-Category pairs 37 | ones = np.ones(R_train.shape[0]) 38 | prod_cat = dict(zip(zip(R_train[:, 1], R_train[:, 3]), ones)) 39 | # Making the mu matrix 40 | mu = np.zeros(1) 41 | cat_rating = R_train[:, 2] 42 | mu[0] = np.mean(cat_rating) 43 | pdb.set_trace() 44 | return R_train, R_test, W, prod_cat, mu 45 | 46 | if __name__ == "__main__": 47 | data = data_handler("../data/rating_with_timestamp.mat", "../data/trust.mat") 48 | R_train, R_test, W, PF_pair, mu = data.load_matrices() 49 | print "done" 50 | -------------------------------------------------------------------------------- /code/simple_rbm.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | from __future__ import division 3 | import numpy as np 4 | import pdb 5 | from data import get_data 6 | 7 | N_IT = 200 8 | ETA = 0.01 9 | 10 | def sigmoid(x): 11 | return 1 / (1 + np.exp(-x)) 12 | 13 | 14 | class RBM(): 15 | 16 | def __init__(self, data): 17 | 18 | self.F = 100 19 | self.K = 5 20 | self.m = 3 21 | self.h = np.random.rand(self.F) 22 | self.featureBias = np.random.rand(self.F) 23 | self.movieBias = np.random.rand(self.m, self.K) 24 | self.w = np.random.rand(self.F, self.m, self.K) 25 | self.data = data 26 | 27 | 28 | def train(self): 29 | 30 | for it in range(N_IT): 31 | posAssociations, self.h = self.fwdProp(self.data) 32 | visibleProb = self.bwdProp(self.h) 33 | negAssociations, temp = self.fwdProp(visibleProb) 34 | self.w += ETA * (posAssociations - negAssociations) / len(self.data) #might change len 35 | error = np.sum((self.data - visibleProb) ** 2) 36 | print error 37 | 38 | 39 | def fwdProp(self, inp): 40 | 41 | hiddenUnit = np.copy(self.featureBias) 42 | for j in range(self.F): 43 | hiddenUnit[j] += np.tensordot(inp, self.w[j]) 44 | hiddenProb = sigmoid(hiddenUnit) 45 | hiddenStates = hiddenProb > np.random.rand(self.F) 46 | hiddenAssociations = np.zeros_like(self.w) 47 | for j in range(self.F): 48 | hiddenAssociations[j] = hiddenProb[j] * inp 49 | return hiddenAssociations, hiddenStates 50 | 51 | 52 | def bwdProp(self, inp): 53 | 54 | visibleUnit = np.copy(self.movieBias) 55 | for j in range(self.F): 56 | visibleUnit += inp[j] * self.w[j] 57 | visibleProb = sigmoid(visibleUnit) 58 | return visibleProb 59 | 60 | 61 | 62 | if __name__ == '__main__': 63 | raw_data = get_data() 64 | all_users = raw_data.keys() 65 | all_users.sort() 66 | user_movies = {} 67 | 68 | data = {} 69 | for u in all_users: 70 | user_movies[u] = [movie_id for (movie_id, rating) in raw_data[u]] 71 | data[u] = [[0]*(rat-1) + [1] + [0]*(5-rat) for (mov_id, rat) in raw_data[u]] 72 | data[u] = np.asarray(data) 73 | break 74 | #data = [[0,0,1,0,0], [0,1,0,0,0], [0,0,0,0,1]] 75 | #data = np.asarray(data) 76 | rbm = RBM(data) 77 | rbm.train() 78 | 79 | 80 | -------------------------------------------------------------------------------- /code/model.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | from __future__ import division 3 | import numpy as np 4 | import pdb 5 | import json 6 | from data import get_data 7 | import copy 8 | 9 | N_IT = 1 10 | ETA = 0.001 11 | 12 | users = {} 13 | user_movies = {} 14 | 15 | def sigmoid(x): 16 | return 1 / (1 + np.exp(-x)) 17 | 18 | 19 | class RBM(): 20 | 21 | def __init__(self, data): 22 | 23 | self.F = 100 24 | self.K = 5 25 | self.m = 0 26 | #self.m = data.shape[0] # No of movies rated by user 27 | for i, u in enumerate(users): 28 | temp = [movie_id for (movie_id, rating) in raw_data[u]] 29 | self.m = max(self.m, max(i for i in temp) + 1) 30 | print self.m 31 | self.h = np.random.rand(self.F) - 0.5 32 | self.featureBias = np.random.rand(self.F) - 0.5 33 | self.movieBias = np.random.rand(self.m, self.K) - 0.5 34 | self.w = np.random.rand(self.F, self.m, self.K) - 0.5 35 | self.data = data 36 | 37 | 38 | def train(self): 39 | for it in range(N_IT): 40 | for u in users: 41 | data = copy.deepcopy(self.data[u]) 42 | w = self.getW(user_movies[u]) 43 | posAssociations, self.h = self.fwdProp(data, user_movies[u]) 44 | visibleProb = self.bwdProp(self.h, user_movies[u]) 45 | negAssociations, temp = self.fwdProp(visibleProb, user_movies[u]) 46 | w += ETA * (posAssociations - negAssociations) / len(user_movies[u]) #might change len 47 | self.setW(user_movies[u], w) 48 | error = np.sum((data - visibleProb) ** 2) 49 | error = np.sqrt(error/len(data)) 50 | print it, u, error 51 | 52 | 53 | def getW(self, movies): 54 | 55 | a = np.zeros((self.F, 1, self.K)) 56 | for m in movies: 57 | a = np.concatenate((a, np.expand_dims(self.w[:,m,:], axis=1)), axis=1) 58 | return a[:,1:,] 59 | 60 | 61 | def setW(self, movies, w): 62 | 63 | it = 0 64 | for m in movies: 65 | self.w[:, m, :] = w[:, it, :] 66 | it += 1 67 | 68 | 69 | def getMovieBias(self, movies): 70 | 71 | a = np.zeros((1, self.K)) 72 | for m in movies: 73 | a = np.concatenate((a, np.expand_dims(self.movieBias[m,:], axis=0)), axis=0) 74 | return a[1:,] 75 | 76 | 77 | def fwdProp(self, inp, movies): 78 | hiddenUnit = np.copy(self.featureBias) 79 | for j in range(self.F): 80 | hiddenUnit[j] += np.tensordot(inp, self.getW(movies)[j]) 81 | hiddenProb = sigmoid(hiddenUnit) 82 | hiddenStates = hiddenProb > np.random.rand(self.F) 83 | hiddenAssociations = np.zeros((self.F, len(movies), self.K)) # Same as self.w for a single user case 84 | for j in range(self.F): 85 | hiddenAssociations[j] = hiddenProb[j] * inp 86 | return hiddenAssociations, hiddenStates 87 | 88 | 89 | def bwdProp(self, inp, movies): 90 | visibleUnit = self.getMovieBias(movies) 91 | for j in range(self.F): 92 | visibleUnit += inp[j] * self.getW(movies)[j] 93 | visibleProb = sigmoid(visibleUnit) 94 | return visibleProb 95 | 96 | 97 | def predictor(self, movie_id, user_id): 98 | 99 | w = self.getW(user_movies[user_id]) 100 | 101 | #making predictions part Vq not given 102 | data = copy.deepcopy(self.data[user_id]) 103 | probs = np.ones(5) 104 | 105 | mx, index = -1, 0 106 | 107 | for i in range(5): 108 | calc = 1.0 109 | for j in range(self.F): 110 | temp = np.tensordot(data, self.getW(user_movies[user_id])[j]) + self.featureBias[j] 111 | temp = 1.0 + np.exp(temp) 112 | calc *= temp 113 | probs[i] = calc 114 | 115 | if mx < probs[i]: 116 | index = i 117 | mx = probs[i] 118 | 119 | return index 120 | 121 | def demo(): 122 | data = [[0,0,1,0,0], [0,1,0,0,0], [0,0,0,0,1]] 123 | data = np.asarray(data) 124 | rbm = RBM(data) 125 | rbm.train() 126 | 127 | 128 | if __name__ == '__main__': 129 | #demo() 130 | 131 | raw_data1 = get_data() 132 | raw_data = {} 133 | ct = 0 134 | for i in raw_data1.keys(): 135 | raw_data[i] = raw_data1[i] 136 | ct += 1 137 | if ct >= 1000: 138 | pdb.set_trace() 139 | break 140 | #json.dump(raw_data, 'data.json') 141 | #raw_data = json.load(open('data.json')) 142 | print 'Data done' 143 | users = raw_data.keys() 144 | users.sort() 145 | data = {} 146 | for i, u in enumerate(users): 147 | user_movies[u] = [movie_id for (movie_id, rating) in raw_data[u]] 148 | data[u] = [[0]*(rat-1) + [1] + [0]*(5-rat) for (mov_id, rat) in raw_data[u]] 149 | data[u] = np.asarray(data[u]) 150 | #rbm = RBM(data[data.keys()]) 151 | rbm = RBM(data) 152 | print 'Training DONE!' 153 | rbm.train() 154 | pdb.set_trace() 155 | -------------------------------------------------------------------------------- /code/model2.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | 3 | from __future__ import division 4 | import numpy as np 5 | from data_handler import data_handler 6 | import math 7 | import pdb 8 | import sys 9 | import copy 10 | from data import get_data 11 | 12 | class baseline_tensor(): 13 | 14 | def __init__(self): 15 | # Loading matrices from given data 16 | data = data_handler() 17 | #data = data_handler("../data/ciao/rating_with_timestamp.mat", "../data/ciao/trust.mat") 18 | self.R_train, self.R_test, self.W, self.PF, self.mu = data.load_matrices() 19 | # Getting unique users and products used in Training data 20 | self.prod = np.unique(self.R_train[:, 1]) 21 | self.users = np.unique(self.R_train[:, 0]) 22 | self.n_users, self.n_prod, self.n_cat = data.get_stats() 23 | common_users = np.intersect1d(self.R_test[:, 0], self.users) 24 | common_prod = np.intersect1d(self.R_test[:, 1], self.prod) 25 | self.R_test = self.R_test[np.in1d(self.R_test[:, 0], common_users)] 26 | self.R_test = self.R_test[np.in1d(self.R_test[:, 1], common_prod)] 27 | # Creating R_train and R_test dictionaries 28 | self.R_train_ui = dict(zip(zip(self.R_train[:, 0], self.R_train[:, 1]), self.R_train[:, 2])) 29 | self.R_test_ui = dict(zip(zip(self.R_test[:, 0], self.R_test[:, 1]), self.R_test[:, 2])) 30 | # Initializing parameters to be estimated 31 | self.A = np.zeros((self.n_users + 1, self.n_users + 1, self.n_cat)) 32 | for u, v in self.W: 33 | self.A[v, u, :] = np.random.rand() 34 | self.B = np.random.rand(self.n_users + 1, self.n_cat) 35 | self.C = np.random.rand(self.n_prod + 1) 36 | self.E = copy.deepcopy(self.R_train_ui) 37 | self.V = {} 38 | self.getNui() 39 | #self.getNuiFromData() 40 | self.alpha = 0.3 41 | self.l = 0.1 42 | 43 | def calc_cost(self): 44 | cost = sum(self.E[key] * self.E[key] for key in self.E) 45 | cost += self.l*(np.vdot(self.B, self.B) + np.vdot(self.C, self.C)) 46 | return cost 47 | 48 | def getNuiFromData(self): 49 | with open('../data/nui') as f: 50 | #with open('../data/ciao/ciaonui') as f: 51 | content = f.readlines() 52 | for line in content: 53 | line = line.split('\n')[0] 54 | value = map(int, line.split('[')[1].split(']')[0].split()) 55 | key = (int(line.split(' ')[0]), int(line.split(' ')[1])) 56 | self.V[key] = value 57 | 58 | def getNui(self): 59 | # Generates Users who are trusted by user u and have rated product i 60 | sz = len(self.R_train_ui) + len(self.R_test_ui) 61 | for u, i in self.R_train_ui: 62 | # Users who have rated product i 63 | rat_u = self.R_train[np.where(self.R_train[:, 1] == i), 0] 64 | # Users trusted by u 65 | trust_u = self.W[np.where(self.W[:, 0] == u),1] 66 | self.V[u, i] = np.intersect1d(rat_u, trust_u) 67 | print u,i,self.V[u, i] 68 | 69 | for u, i in self.R_test_ui: 70 | # Users who have rated product i 71 | rat_u = self.R_train[np.where(self.R_train[:, 1] == i), 0] 72 | # Users trusted by u 73 | trust_u = self.W[np.where(self.W[:, 0] == u),1] 74 | self.V[u, i] = np.intersect1d(rat_u, trust_u) 75 | print u,i,self.V[u, i] 76 | 77 | def calculateRcap(self, u, i): 78 | # First part of Rcap 79 | n1 = 0 80 | d1 = 1 81 | for k in xrange(self.n_cat): 82 | if (i,k) in self.PF: 83 | n1 += (self.mu[k] + self.B[u, k]) 84 | d1 += 1 85 | Rcap = self.alpha * ((n1/d1) + self.C[i]) 86 | # Second part of Rcap 87 | n2 = 0 88 | d2 = 1 89 | for k in xrange(self.n_cat): 90 | if((i,k) in self.PF): 91 | for v in self.V[u, i]: 92 | n2 += (self.R_train_ui[v, i] * self.A[v, u, k]) 93 | d2 += self.A[v, u, k] 94 | if d2 != 0: 95 | Rcap += ((1 - self.alpha) * (n2/d2)) 96 | # Defining p and q 97 | self.p = d2 98 | self.q = n2 99 | return Rcap 100 | 101 | def model(self, lr_a = 0.1, lr_b = 0.1, lr_c = 0.1, n_it = 150): 102 | # Optimization Routine 103 | self.Rcap = np.zeros_like(self.R_train_ui) 104 | for it in xrange(n_it): 105 | cost = self.calc_cost() 106 | for u, i in self.R_train_ui: 107 | Rcap = self.calculateRcap(u, i) 108 | # Defining E 109 | self.E[u, i] = self.R_train_ui[u, i] - Rcap 110 | # Updating C 111 | grad_c = (-self.alpha * self.E[u, i]) + (self.l * self.C[i]) 112 | self.C[i] -= (lr_c * grad_c) 113 | # Updating B 114 | d = 0 115 | for k in xrange(self.n_cat): 116 | if (i,k) in self.PF: 117 | d += 1 118 | grad_b = (-self.alpha * self.E[u, i]) + (self.l * (self.B[u, :]/d)) 119 | #grad_b = -self.alpha * (self.E[u, i]/d) + self.l * self.B[u, :] 120 | self.B[u, :] -= (lr_b * grad_b) 121 | # Updating A 122 | for k in xrange(self.n_cat): 123 | if (i,k) in self.PF: 124 | for v in self.V[u, i]: 125 | if self.p != 0: 126 | grad_a = ((self.alpha - 1) * self.E[u,i] ) * (((self.p * self.R_train_ui[v, i]) - self.q)/(self.p * self.p)) 127 | self.A[v, u, k] -= (lr_a * grad_a) 128 | if self.A[v, u, k] < 0: 129 | self.A[v, u, k] = 0 130 | elif self.A[v, u, k] > 1: 131 | self.A[v, u, k] = 1 132 | print "It: ", it+1, " Cost(training): ", cost, "RMSE(test): ", self.test() 133 | 134 | def test(self): 135 | error = 0 136 | U = 0 137 | for key in self.R_test_ui: 138 | U += 1 139 | u, i = key 140 | R_Rcap = self.R_test_ui[u, i] - self.calculateRcap(u, i) 141 | error += (R_Rcap * R_Rcap) 142 | return math.sqrt(error/U) 143 | 144 | if __name__ == "__main__": 145 | model = baseline_tensor() 146 | model.model() 147 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------