File: pq3.py

package info (click to toggle)
python-pqueue 0.2-7.3
  • links: PTS
  • area: main
  • in suites: buster, stretch
  • size: 244 kB
  • ctags: 85
  • sloc: ansic: 660; python: 262; makefile: 41
file content (215 lines) | stat: -rw-r--r-- 7,024 bytes parent folder | download | duplicates (6)
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

""" classical priority queue

 it supports an arbitrary comparison function
 and is good for arbitrary length queues.

Based on pq2.py, optimized (about 3-4x faster).
"""

from bisect import insort

class PQ0:
    """priority queue using insertion sorting (bisect)

       This seems to be the fastest way, unless you want to use
       a different comparison metric, or unless the set grows *very* large.
    """
    def __init__(self, comparison = cmp):
        if comparison!=cmp:
            raise ValueError, "only cmp comparison supported for PQ0"
        self.data = []

    def empty(self):
        return len(self.data)==0

    def addelt(self, priority, elt):
        item = (priority, elt)
        insort(self.data, item)

    def largestp(self):
        return self.data[-1]

    def poplargest(self):
        data = self.data
        item = data[-1]
        del data[-1]
        return item

    def popsmallest(self):
        data = self.data
        item = data[0]
        del data[0]
        return item

class PQueue:
    """basic priority queue, using classical heap structure"""

    def __init__(self, comparison = cmp):
        self.cmp = comparison
        self.data = [None] * 8 # presize for 8 elements
        # first element is always empty, first used is 1, no data yet
        self.free = 1

    def empty(self):
        """empty test"""
        return self.free == 1

    def addelt(self, priority, elt):
        """add element by decreasing priority"""
        index = self.free
        try:
            self.data[ index ] = (priority, elt)
        except IndexError:
            # self.data is too small, double its size
            length = len( self.data )
            newdata = [ None ] * (2 * length)
            # store the old values
            newdata[:length] = self.data
            self.data = newdata
            # now there ought to be room!
            self.data[ index ] = (priority, elt)
        self.free = index + 1
        return self._checkposition(index)

    def popsmallest(self):
        """get/pop element with smallest priority"""
        if self.free < 2:
           raise IndexError, "priority queue is empty"
        smallest = self.data[1]
        self._removeentry(1)
        return smallest

    def _removeentry(self, index):
        """internal: remove an entry"""
        # restructure the "tree" if other elements remain
        free = self.free
        data = self.data
        last = free - 1
        if last > index:
           data[index] = data[last]
           data[last] = None
           self.free = last
           self._checkposition(index)
        else:
           data[index] = None
           self.free = last

    def smallestp(self):
        """get (priority, element) for smallest priority"""
        return self.data[1]
        
    # make sure a position has contents larger than parents,
    # and smaller than children, and if not, do a swap
    # and check the new position recursively...
    #
    def _checkposition(self, index):
        data = self.data
        comparison = self.cmp
        thisitem = data[index]
        thispriority = thisitem[0]
        free = self.free
        if index>=2:
            # check parent, possibly bubble up
            parent = index/2
            parentitem = data[parent]
            parentpriority = parentitem[0]
            if comparison(parentpriority, thispriority)>0:
                while 1:
                    data[parent] = thisitem
                    data[index] = parentitem
                    index = parent
                    if index<2:
                       return index
                    parent = index/2
                    parentitem = data[parent]
                    parentpriority = parentitem[0]
                    if comparison(parentpriority, thispriority)<=0:
                       return index
        # otherwise, check children
        # find the highest priority child:
        while 1:
            thechild = index*2
            if thechild>=free: return index
            childitem = data[thechild]
            childpriority = childitem[0]
            otherchild = thechild+1
            if otherchild<free:
                otherchilditem = data[otherchild]
                otherchildpriority = otherchilditem[0]
                if comparison(otherchildpriority, childpriority)<0:
                    thechild = otherchild
                    childitem = otherchilditem
                    childpriority = otherchildpriority
            # thisitem should be larger than childitem
            if comparison(thispriority, childpriority)<=0:
                return index
            data[index] = childitem
            data[thechild] = thisitem
            index = thechild

    def displaytree(self, index=1, indentlevel=0):
        print "   "*indentlevel, self.data[index], "at", index
        free = self.free
        for child in (index*2, index*2+1):
            if child<free:
               self.displaytree(child, indentlevel+1)

# this mixin must be combined with a "base" priority queue
# implementation.  It groups elements with common priorities.
# It should precede the "base" implementation in the inheritance
# hierarchy.
#
class PQEquivMixin:

   # requires a Q_implementation member

   # this initialization function MUST be called on subclass init.
   def __init__(self, comparison = cmp):
       # initialize self as a self.Q_implementation
       self.Q_implementation.__init__(self, cmp)
       # add a dictionary for holding elements at equivalent priorities
       self.EquivDict = {}

   def addelt(self, priority, elt):
       # is there a class of elements at this priority?
       EquivDict = self.EquivDict
       try:
           list = EquivDict[priority]
           list.append(elt)
       except KeyError:
           # there is none: add this element as a new priority group
           # First: add the priority to the queue
           self.Q_implementation.addelt(self, priority, priority)
           # Then: add the new group to the dictionary
           EquivDict[priority] = [elt]

   def smallest_group_p(self):
       # find the smallest priority
       (priority, dummy) = self.Q_implementation.smallestp(self)
       # return the priority and the associated group
       group = self.EquivDict[priority]
       return (priority, group)

   def smallest_p(self):
       (priority, group) = self.smallest_group_p()
       return (priority, group[-1])

   def popsmallest(self):
       (priority, group) = self.smallest_group_p()
       chosen = group[-1]
       del group[-1]
       # if group is now empty then delete this priority
       if group == []:
          del self.EquivDict[priority]
          self.Q_implementation.popsmallest(self)
       return (priority, chosen)

   def popsmallest_group(self):
       (priority, group) = self.smallest_group_p()
       self.Q_implementation.popsmallest(self)
       return group

# using the mixin:
class PQEquivBig(PQEquivMixin, PQueue):
   Q_implementation = PQueue