First commit

knnqueries.py

@@ -0,0 +1,116 @@
+import sys
+import ast
+import heapq as hq
+from math import sqrt
+
+class Point:
+	def __init__(self, x, y):
+		self.x = x
+		self.y = y
+
+class MBR:
+    def __init__(self, id, xlow, xhigh, ylow, yhigh):
+        self.xlow = xlow
+        self.xhigh = xhigh
+        self.ylow = ylow
+        self.yhigh = yhigh
+        self.id = id
+        self.obj = False
+
+    def setDistance(self, qp):
+        self.distance = self.findDistance(qp)
+
+    def findDistance(self, qp):
+        dx = dy = 0
+        if qp.x < self.xlow:
+            dx = self.xlow - qp.x
+        elif qp.x > self.xhigh:
+            dx = qp.x - self.xhigh
+        else:
+            dx = 0
+
+        if qp.y < self.ylow:
+            dy = self.ylow - qp.y
+        elif qp.y > self.yhigh:
+            dy = qp.y - self.yhigh
+        else:
+            dy = 0
+        return sqrt(dx**2 + dy**2)
+
+    def makeObject(self):
+        self.obj = True
+
+    def __lt__(self, other):
+        if self.distance < other.distance:
+            return True
+        return False
+
+class Node:
+    def __init__(self, leaf, id, list_of_mbrs):
+        self.leaf = leaf
+        self.id = id
+        self.list_of_mbrs = list_of_mbrs
+
+def parseRtree(filename):
+    with open(filename, 'r') as f:
+        nodes = []
+        for line in f:
+            node = ast.literal_eval(line)
+            mbrs = []
+            for mbr in node[2]:
+                mbrs.append(MBR(int(mbr[0]), float(mbr[1][0]), float(mbr[1][1]),
+                    float(mbr[1][2]), float(mbr[1][3])))
+            nodes.append(Node(node[0], node[1], mbrs))
+
+    return nodes
+
+# Read and execute all the NN queries assigned to the r tree
+def parseQuery(rtree, filename, k):
+    with open(filename, 'r') as f:
+        num = 0
+        for line in f:
+            [x, y] = line.split()
+            findKNN(rtree, Point(float(x), float(y)), k, num)
+            num += 1
+
+# Search for the neareset k neighboors
+def findKNN(rtree, qp, k, num):
+    # List to put our k nn objects
+    results = []
+    # Initialize a priority queue
+    pq = []
+    # Put the root nodes into the priority queue
+    for mbr in rtree[-1].list_of_mbrs:
+        mbr.setDistance(qp)
+        hq.heappush(pq, mbr)
+
+    while len(pq) > 0 and len(results) < k:
+        # Retrieve the next node and remove it from the queue
+        e = hq.heappop(pq)
+
+        if e.obj == True:
+            hq.heappush(results, e)
+            continue
+
+        # At a leaf node
+        if rtree[e.id].leaf == 0:
+            for mbr in rtree[e.id].list_of_mbrs:
+                mbr.setDistance(qp)
+                mbr.makeObject()
+                hq.heappush(pq, mbr)
+        # At an intermediate node
+        else:
+            for node in rtree[e.id].list_of_mbrs:
+                node.setDistance(qp)
+                hq.heappush(pq, node)
+
+    print("{}:".format(num), end=' ')
+    for r in range(k-1):
+        nn = hq.heappop(results)
+        print("{},".format(nn.id), end='')
+    nn = hq.heappop(results)
+    print(nn.id)
+
+if __name__ == '__main__':
+    rtree = parseRtree(sys.argv[1])
+    parseQuery(rtree, sys.argv[2], int(sys.argv[3]))