-
Notifications
You must be signed in to change notification settings - Fork 0
/
hashTable.cpp
257 lines (207 loc) · 5.58 KB
/
hashTable.cpp
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
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
#include <iostream>
#include <cmath>
#include <cstdlib>
#include <utility>
#include <map>
#include "dataStructs.hpp"
#include "hashTable.hpp"
using namespace std;
template <>
void HashTable<map<unsigned int, char> >::initialize(size_t size, int givenw, int givenNumFunct, int givendim, int givenMaxPoints)
{
numFunct = givenNumFunct;
w = givenw;
dimensions = givendim;
maxPoints = givenMaxPoints;
// create buckets
buckets = new map<unsigned int, char>;
// initialize M
unsigned int temp = 32 / numFunct;
M = 1;
M = M << temp;
// create si arrays (fill with uniformly random numbers from 0 to w)
sVectors = new double *[numFunct];
for (int i = 0; i < numFunct; i++)
{
sVectors[i] = new double[dimensions];
}
uniform_real_distribution<> dis(0.0, (double)w);
for (int i = 0; i < numFunct; i++)
{
for (int j = 0; j < dimensions; j++)
{
sVectors[i][j] = dis(generator);
}
}
// create array to keep powers of m
mArray = new unsigned int[dimensions];
unsigned int m = INT32_MAX - 4;
for (int i = 0; i < dimensions; i++)
{
mArray[i] = modular_expo(m, i, M);
}
}
template <class T>
void HashTable<T>::initialize(size_t size, int givenw, int givenNumFunct, int givendim, int givenMaxPoints)
{
numFunct = givenNumFunct;
w = givenw;
dimensions = givendim;
maxPoints = givenMaxPoints;
// create buckets
bucketSize = size;
buckets = new T[bucketSize];
// initialize M
unsigned int temp = 32 / numFunct;
M = 1;
M = M << temp;
// create si arrays (fill with uniformly random numbers from 0 to w)
sVectors = new double *[numFunct];
for (int i = 0; i < numFunct; i++)
{
sVectors[i] = new double[dimensions];
}
uniform_real_distribution<> dis(0.0, (double)w);
for (int i = 0; i < numFunct; i++)
{
for (int j = 0; j < dimensions; j++)
{
sVectors[i][j] = dis(generator);
}
}
// create array to keep powers of m
mArray = new unsigned int[dimensions];
unsigned int m = INT32_MAX - 4;
for (int i = 0; i < dimensions; i++)
{
mArray[i] = modular_expo(m, i, M);
}
}
template <>
void HashTable<vector<pair<class Point *, unsigned int> > >::printHashTable()
{
for (int i = 0; i < bucketSize; i++)
{
cout << buckets[i].size() << " ";
}
cout << endl;
}
template <class T>
void HashTable<T>::printHashTable()
{
}
template <class T>
HashTable<T>::HashTable() : generator((std::random_device())())
{
}
template <>
HashTable<map<unsigned int, char> >::~HashTable()
{
//clear the structures and the allocated memory
buckets->clear();
delete buckets;
for (int i = 0; i < numFunct; i++)
{
delete[] sVectors[i];
}
delete[] sVectors;
delete[] mArray;
}
template <class T>
HashTable<T>::~HashTable()
{
delete[] buckets;
for (int i = 0; i < numFunct; i++)
{
delete[] sVectors[i];
}
delete[] sVectors;
delete[] mArray;
}
template <>
int HashTable<vector<pair<class Point *, unsigned int> > >::insertPoint(class Point *point)
{
unsigned int result = amplifiedHashFunctionPoint(point);
//insert point to the dataset to the position indicated by the hashFunction
pair<class Point *, unsigned int> PAIR = make_pair(point, result);
unsigned int position = result % bucketSize;
buckets[position].push_back(PAIR);
return 0;
}
template <>
int HashTable<map<unsigned int, char> >::insertPoint(class Point *point)
{
unsigned int result = amplifiedHashFunctionPoint(point);
char random;
map<unsigned int, char>::iterator it;
it = buckets->find(result);
if (it == buckets->end())
{
uniform_int_distribution<> dis(0, 1);
random = dis(generator);
buckets->insert(pair<unsigned int, unsigned int>(result, random));
return random;
}
else
{
return it->second;
}
}
template <class T>
int HashTable<T>::insertPoint(class Point *point)
{
return 0;
};
template <class T>
unsigned int HashTable<T>::amplifiedHashFunctionPoint(class Point *x)
{
int shiftAmount = 32 / numFunct;
unsigned int temp, result = 0;
for (int i = 0; i < numFunct; i++)
{
temp = hashFunctionPoint(x, i);
temp = temp << shiftAmount * i; //prepare the 32/numFunct binary digits for concatenation
result += temp;
}
return result;
}
template <class T>
unsigned int HashTable<T>::hashFunctionPoint(class Point *x, int functionNo)
{
int *a = new int[dimensions];
double *s = sVectors[functionNo];
unsigned int m = UINT32_MAX - 4;
for (int i = 0; i < dimensions; i++)
{
a[i] = (int)floor((x->getCoord()[i] - s[i]) / w);
}
unsigned int result = 0;
unsigned int step1;
for (int i = 0; i < dimensions; i++)
{
step1 = (a[dimensions - 1 - i] % M + M) % M;
result += (step1 * mArray[i]) % M;
}
result = result % M;
delete[] a;
return result;
}
unsigned int modular_expo(unsigned int base, unsigned int exponent, unsigned int modulus)
{
//function that calculates modulos of exponentials that produce very big values
if (modulus == 1)
return 0;
if (exponent == 0)
if (modulus == 1)
return 1;
else
return 0;
unsigned long long c = 1;
for (unsigned int i = 1; i <= exponent; i++)
{
c = (c * base) % modulus;
}
return c;
}
template class HashTable<vector<pair<class Point *, unsigned int> > >;
template class HashTable<map<unsigned int, char> >;