Pruthuvi Maheshakya Wijewardena maheshakya

## Decision_tree_imp.ipynb

      
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## KDDcup_2014_analysis.ipynb

      
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## compare_ANN_v2.py
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

## dummy_data_sest.csv

          
            6
            148
            72
            35
            0
            336
            627
            50
            1

            
              1
              85
              66
              29
              0
              266
              351
              31
              0

            
              8
              183
              64
              0
              0
              233
              672
              32
              1

            
              1
              89
              66
              23
              94
              281
              167
              21
              0

            
              0
              137
              40
              35
              168
              431
              2288
              33
              1

            
              5
              116
              74
              0
              0
              256
              201
              30
              0

            
              3
              78
              50
              32
              88
              310
              248
              26
              1

            
              10
              115
              0
              0
              0
              353
              134
              29
              0

            
              2
              197
              70
              45
              543
              305
              158
              53
              1

            
              8
              125
              96
              0
              0
              0
              232
              54
              1

## compare_ANN.py
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

## kdd2014_hack.py
"""
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

## LSH_forest_hack.py
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:

## GetValues.java
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." ;

## svd_processing.py
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

## bit_strings_query.py
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
	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
6	148	72	35	0	336	627	50	1
1	85	66	29	0	266	351	31	0
8	183	64	0	0	233	672	32	1
1	89	66	23	94	281	167	21	0
0	137	40	35	168	431	2288	33	1
5	116	74	0	0	256	201	30	0
3	78	50	32	88	310	248	26	1
10	115	0	0	0	353	134	29	0
2	197	70	45	543	305	158	53	1
8	125	96	0	0	0	232	54	1
	"""
	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
	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:
	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." ;
	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