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# a: array of hashes
# h: number of hash bits to be considered
import numpy as np

class Node():
    """"
    Defines a node in the prefix tree
    """
    def __init__(self, value):
        self.children = list()

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import numpy as np

#Re-implementation of bisect functions of bisect module to suit the application
def bisect_left(a, x):
    lo = 0
    hi = len(a)
    while lo < hi:
        mid = (lo+hi)//2
        if a[mid] < x:
            lo = mid + 1

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from scipy import sparse as sp
import pandas as pd
import numpy as np
from scipy.sparse.linalg import svds
import pickle

#loads data from movielens data matrix.
#After extracting the compressed file, you will get a ratings.dat file
#movilelens site(http://grouplens.org/datasets/movielens/) has all information you need to read
#here I have used 10M data set

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import org.wso2.carbon.governance.api.exception.GovernanceException;
import org.wso2.carbon.registry.core.Resource;
import java.util.Enumeration;
 
class SomeClass(){
 
    private final String REGISTRY_LC_NAME = "registry.LC.name";
    private final String REGISTRY_LIFECYCLE = "registry.lifecycle.";
    private final String REGISTRY_CUSTOM_LIFECYCLE_CHECKLIST = "registry.custom_lifecycle.checklist.option.";
    private final String REGISTRY_CUSTOM_LIFECYCLE_VOTE = "registry.custom_lifecycle.votes.option." ;

maheshakya

import numpy as np
from sklearn.metrics import euclidean_distances

#Re-implementation of bisect functions of bisect module to suit the application
def bisect_left(a, x):
    lo = 0
    hi = len(a)
    while lo < hi:
        mid = (lo+hi)//2
        if a[mid] < x:

maheshakya

"""
Dependencies: Python 2.7 or higher, numpy, scikit-learn
"""

import pandas as pd
import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder, OneHotEncoder

maheshakya

import time
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from sklearn.neighbors import LSHForest
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import normalize
from annoy import AnnoyIndex
from pyflann import FLANN

n_iter = 100

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maheshakya

import time
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import LSHForest
from annoy import AnnoyIndex
from pyflann import FLANN

n_iter = 5100
n_neighbors = 10

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