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Commit ca19b8a5 authored by Maria Hartmann's avatar Maria Hartmann
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code cleanup

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scripts/paper-dijkstra-test.py
+ 17
− 44
View file @ ca19b8a5
......@@ -25,47 +25,25 @@ COUNT_TYPE_COMPS = -2
TYPES = {21: "Pairing", 22: "Smooth"}
MAX_TYPE_KEY = max(TYPES.keys())
#COLOURS = ['xkcd:fire engine red', 'xkcd:dusty orange', 'xkcd:clear blue', 'xkcd:cool green',
# 'xkcd:macaroni and cheese', 'xkcd:fire engine red', 'xkcd:dusty orange', 'xkcd:clear blue',
# 'xkcd:cool green', 'xkcd:macaroni and cheese', 'xkcd:bright sky blue', 'xkcd:bright sky blue', 'xkcd:green',
# 'xkcd:ochre', 'xkcd:sea blue', 'xkcd:sea green', 'xkcd:sea blue', 'xkcd:warm grey',
# 'xkcd:bright sky blue', 'xkcd:bright sky blue']
LINETYPES = 5 * ['-'] + 5 * ['--'] + ['--', '-'] + ['--', '--', '--', '--', '--', '--', '-', '-']
FIG_LABELS = ["comparisons", "links"]
MAX_TYPE_KEY = max(TYPES.keys())
COLOURS = {21:'xkcd:fire engine red', 22:'xkcd:sea green'}
SHADE_COLOURS = {21: '#fe4d4e', 22: '#58ab8e'}
NUMBER_TESTS = 10 # number of tests to run
TEST_SIZE = 500 # ,6000,7000,8000,9000,10000,20000,30000,40000,50000,60000,70000,80000,90000,100000
TEST_SIZE = 500
EDGE_PROBABILITY = 0.05
WEIGHT_RANGE = 10000
def plot_avg_counts_old(avgCounts):
# colours from https://xkcd.com/color/rgb/
linetypes = 5 * ["-"] + 5 * ["--"] + ["--", "-"] + ["-, -"]
plt.figure('Dijkstra with variable connectivity')
for k in TYPES.keys():
avgComps = [acounts[k] for acounts in avgCounts[0]]
plt.plot([factor * EDGE_PROBABILITY for factor in range(1, 101, 1)], avgComps, color=COLOURS[k], linestyle='-',
label=TYPES[k] + " comparisons")
avgLinks = [acounts[k] for acounts in avgCounts[1]]
plt.plot([factor * EDGE_PROBABILITY for factor in range(1, 101, 1)], avgLinks, color=COLOURS[k], linestyle='--',
label=TYPES[k] + " links")
plt.grid(True)
plt.legend(loc='best')
plt.show()
def plot_avg_counts(avgCounts):
"""generates and saves plot of results"""
# colours from https://xkcd.com/color/rgb/
MARKERS_COMP = {21:"o", 12:"d", 22:"^"}#https://matplotlib.org/3.1.1/api/markers_api.html
MARKERS_LINK = {21:"o", 12:"D", 22:"D"}
plt.figure('avg number of operations in Dijkstra\'s algorithm')
deviations = [factor * EDGE_PROBABILITY for factor in range(1, 21, 1)]
for k in TYPES.keys():
#print(k)
avgComps = [acounts[k] for acounts in avgCounts[0]]
maxComps = [acounts[k] for acounts in avgCounts[2]]
minComps = [acounts[k] for acounts in avgCounts[4]]
......@@ -77,7 +55,6 @@ def plot_avg_counts(avgCounts):
plt.plot(deviations, avgLinks, color=COLOURS[k], linestyle="--", marker=MARKERS_LINK[k], markerfacecolor=COLOURS[k], markersize=9, markeredgewidth=1, markeredgecolor='black', label=TYPES[k] + " links")
plt.fill_between(deviations, minLinks, maxLinks, color=SHADE_COLOURS[k], alpha=.3)
plt.xlabel('Edge probability', fontsize=26)
plt.ylabel('Avg. number of operations / size', fontsize=26)
plt.xticks(fontsize=20)
......@@ -85,7 +62,6 @@ def plot_avg_counts(avgCounts):
plt.rc('legend',fontsize=26) # using a size in points
plt.legend()
plt.grid(True)
#plt.gca().invert_xaxis()
figure = plt.gcf() # get current figure
figure.set_size_inches(16, 18) # set figure's size manually to full screen
plt.savefig('../plots/paper-dijkstra-new.svg', bbox_inches='tight') # bbox_inches removes extra white spaces
......@@ -143,26 +119,25 @@ if __name__ == "__main__":
for _ in range(NUMBER_TESTS):
# some nice graph generators here: https://networkx.github.io/documentation/stable/reference/generators.html
# initialize input graph
graph = nx.fast_gnp_random_graph(TEST_SIZE, x)
# graph = nx.random_regular_graph(10, 1000)
for (u, v) in graph.edges():
for (u, v) in graph.edges(): # assign weights
graph.edges[u, v]['w'] = random.randint(1, WEIGHT_RANGE)
for heapType in TYPES.keys():
for v in graph.nodes():
graph.nodes[v]['v'] = False # "visited" marker
linkCount = 0
compCount = 0
vertex2qnode = {}
vertex2qnode = {} # mapping graph nodes to heap nodes
dist = [888888888 for _ in range(len(graph.nodes()))]
prev = [None for _ in range(len(graph.nodes()))]
heap = PairingHeap(heapType, COUNT_TYPE_BOTH)
heap.make_heap()
source = graph.nodes()[0]
dist[0] = 0
# Dijkstra's algorithm
dist[0] = 0 # start node
for idx, v in enumerate(graph.nodes()):
#qnode = Node(dist[idx])
qnode = Node(dist[v])
qnode.vertex = v
vertex2qnode[v] = qnode
......@@ -180,18 +155,16 @@ if __name__ == "__main__":
s))
u = minNode.vertex
uk = minNode.key
#print(uk)
#print('extracted {}'.format(minNode.key))
graph.nodes[u]['v'] = True # minNode has been visited
for idx, v in enumerate(graph.neighbors(u)):
alt = uk + graph.edges[u, v]['w']
if alt < dist[v] and not graph.nodes[v]['v']:
(cc, lc) = heap.decrease_key(vertex2qnode[v], dist[v] - alt)
#print('decreased from {} to {}'.format(dist[v],alt))
linkCount += lc
compCount += cc
dist[v] = alt
prev[v] = u
# track avg. results
avgCountsLinks[heapType] += (linkCount / NUMBER_TESTS)/TEST_SIZE
avgCountsComps[heapType] += (compCount / NUMBER_TESTS)/TEST_SIZE
maxCountsLinks[heapType] = max(maxCountsLinks[heapType], linkCount/TEST_SIZE)
......@@ -212,6 +185,6 @@ if __name__ == "__main__":
maxCompsPerSize += [maxCountsComps]
minLinksPerSize += [minCountsLinks]
minCompsPerSize += [minCountsComps]
# plot_avg_counts([avgCompsPerSize, avgLinksPerSize])
plot_avg_counts([avgCompsPerSize, avgLinksPerSize, maxCompsPerSize, maxLinksPerSize, minCompsPerSize, minLinksPerSize])
export_results(xs, [avgCompsPerSize, avgLinksPerSize], COUNT_TYPE_BOTH, TYPES, "dijkstra")
scripts/paper-dijkstra-test2.py
+ 18
− 42
View file @ ca19b8a5
......@@ -4,7 +4,11 @@ as priority queue in Dijkstra's algorithm. Algorithm is run on randomly generate
10-regular graphs of variable size.
Results are stored as .csv files in ../data folder and plots of results in ../plots"""
import networkx as nx
import random
import matplotlib.pyplot as plt
import psutil
import csv
import os, sys, inspect
# ensuring imports from parent directory work
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
......@@ -12,11 +16,6 @@ parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
from node import Node
from pairing_heap import PairingHeap
import networkx as nx
import random
import matplotlib.pyplot as plt
import psutil
import csv
COUNT_TYPE_BOTH = 0
COUNT_TYPE_LINKS = -1
......@@ -24,9 +23,7 @@ COUNT_TYPE_COMPS = -2
TYPES = {21: "Pairing", 22: "Smooth"}
MAX_TYPE_KEY = max(TYPES.keys())
LINETYPES = 5 * ['-'] + 5 * ['--'] + ['--', '-'] + ['--', '--', '--', '--', '--', '--', '-', '-']
FIG_LABELS = ["comparisons", "links"]
MAX_TYPE_KEY = max(TYPES.keys())
# colours from https://xkcd.com/color/rgb/
COLOURS = {21:'xkcd:fire engine red', 22:'xkcd:sea green'}
......@@ -38,26 +35,12 @@ EDGE_PROBABILITY = 0.05
WEIGHT_RANGE = 10000
def plot_avg_counts_old(avgCounts):
plt.figure('Dijkstra with variable connectivity')
for k in TYPES.keys():
avgComps = [acounts[k] for acounts in avgCounts[0]]
plt.plot([factor * EDGE_PROBABILITY for factor in range(1, 101, 1)], avgComps, color=COLOURS[k], linestyle='-',
label=TYPES[k] + " comparisons")
avgLinks = [acounts[k] for acounts in avgCounts[1]]
plt.plot([factor * EDGE_PROBABILITY for factor in range(1, 101, 1)], avgLinks, color=COLOURS[k], linestyle='--',
label=TYPES[k] + " links")
plt.grid(True)
plt.legend(loc='best')
plt.show()
def plot_avg_counts(avgCounts):
MARKERS_COMP = {21:"o", 12:"d", 22:"^"}#https://matplotlib.org/3.1.1/api/markers_api.html
MARKERS_LINK = {21:"o", 12:"D", 22:"D"}
plt.figure('avg number of operations in Dijkstra\'s algorithm')
deviations = [ 10+round(TEST_SIZE*20*factor * EDGE_PROBABILITY) for factor in range(1, 21, 1)]
for k in TYPES.keys():
#print(k)
avgComps = [acounts[k] for acounts in avgCounts[0]]
maxComps = [acounts[k] for acounts in avgCounts[2]]
minComps = [acounts[k] for acounts in avgCounts[4]]
......@@ -74,10 +57,8 @@ def plot_avg_counts(avgCounts):
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.rc('legend', fontsize=26) # using a size in points
#plt.gca().invert_xaxis()
plt.legend()
plt.grid(True)
#plt.gca().invert_xaxis()
figure = plt.gcf() # get current figure
figure.set_size_inches(16, 18) # set figure's size manually to full screen
plt.savefig('../plots/paper-dijkstra2-new.svg', bbox_inches='tight') # bbox_inches removes extra white spaces
......@@ -135,13 +116,11 @@ if __name__ == "__main__":
for _ in range(NUMBER_TESTS):
# some nice graph generators here: https://networkx.github.io/documentation/stable/reference/generators.html
##graph = nx.fast_gnp_random_graph(100, 0.1)
# initializing input
graph = nx.random_regular_graph(10,10+round(TEST_SIZE*x*20))
##graph = nx.random_regular_graph(10,100)
for (u, v) in graph.edges():
graph.edges[u, v]['w'] = random.randint(1, WEIGHT_RANGE)
for heapType in TYPES.keys():
#print('---')
for v in graph.nodes():
graph.nodes[v]['v'] = False # "visited" marker
linkCount = 0
......@@ -153,15 +132,13 @@ if __name__ == "__main__":
heap = PairingHeap(heapType, COUNT_TYPE_BOTH)
heap.make_heap()
source = graph.nodes()[0]
# Dijkstra's algorithm
dist[0] = 0
for idx, v in enumerate(graph.nodes()):
# qnode = Node(dist[idx])
qnode = Node(dist[v])
qnode.vertex = v
vertex2qnode[v] = qnode
(cc, lc) = heap.insert(qnode)
#print('inserted key {}',qnode.key)
linkCount += lc
compCount += cc
......@@ -175,18 +152,17 @@ if __name__ == "__main__":
s))
u = minNode.vertex
uk = minNode.key
#print(uk)
#print('extracted {}'.format(minNode.key))
graph.nodes[u]['v'] = True # minNode has been visited
for idx, v in enumerate(graph.neighbors(u)):
alt = uk + graph.edges[u, v]['w']
if alt < dist[v] and not graph.nodes[v]['v']:
(cc, lc) = heap.decrease_key(vertex2qnode[v], dist[v] - alt)
#print('decreased from {} to {}'.format(dist[v],alt))
linkCount += lc
compCount += cc
dist[v] = alt
prev[v] = u
# tracking avg. results
TSIZE = 10+round(TEST_SIZE*x*20)
avgCountsLinks[heapType] += (linkCount / NUMBER_TESTS)/TSIZE
avgCountsComps[heapType] += (compCount / NUMBER_TESTS)/TSIZE
......@@ -208,6 +184,6 @@ if __name__ == "__main__":
maxCompsPerSize += [maxCountsComps]
minLinksPerSize += [minCountsLinks]
minCompsPerSize += [minCountsComps]
# plot_avg_counts([avgCompsPerSize, avgLinksPerSize])
plot_avg_counts([avgCompsPerSize, avgLinksPerSize, maxCompsPerSize, maxLinksPerSize, minCompsPerSize, minLinksPerSize])
export_results(xs, [avgCompsPerSize, avgLinksPerSize], COUNT_TYPE_BOTH, TYPES, "dijkstra")
scripts/paper-sorting-loc.py
+ 3
− 13
View file @ ca19b8a5
......@@ -21,14 +21,8 @@ from pairing_heap import PairingHeap
COUNT_TYPE_BOTH = 0
COUNT_TYPE_LINKS = -1
COUNT_TYPE_COMPS = -2
MAXSIZE = 17
NUMBER_TESTS = 10 # number of tests to run
TEST_SIZES = [j for j in range(MAXSIZE)]
LIST_LEN = 10000 # number of elements in test list
TEST_SIZE = 10000 # number of elements in test list
STEP_SIZE = 100
INCREMENT_LOC = 0.01
INCREMENT_SUBSEQS = 100
TYPES = {0: "Pairing", 12: "Smooth"}
MAX_TYPE_KEY = max(TYPES.keys())
COLOURS = {0: 'xkcd:fire engine red', 12: 'xkcd:green'}
......@@ -47,7 +41,6 @@ def localizedShuffleByIndex(llist, sdev):
tuples += [(key, element)] # store element with key
sortedTuples = sorted(tuples, key=lambda x: x[0]) # sort key-element tuples by keys
sortedList = [tup[1] for tup in sortedTuples] # discard keys
# print(sortedList)
return sortedList
......@@ -59,7 +52,6 @@ def plot_avg_counts(avgCounts):
plt.figure('avg number of operations by heap type')
deviations = [fac * INCREMENT_LOC for fac in range(0, math.ceil(0.3 / INCREMENT_LOC), 1)]
for k in TYPES.keys():
#print(k)
avgComps = [acounts[k] for acounts in avgCounts[0]]
maxComps = [acounts[k] for acounts in avgCounts[2]]
minComps = [acounts[k] for acounts in avgCounts[4]]
......@@ -71,7 +63,6 @@ def plot_avg_counts(avgCounts):
plt.plot(deviations, avgLinks, color=COLOURS[k], linestyle="--", marker=MARKERS_LINK[k], markerfacecolor=COLOURS[k], markersize=9, markeredgewidth=1, markeredgecolor='black', label=TYPES[k] + " links")
plt.fill_between(deviations, minLinks, maxLinks, color=SHADE_COLOURS[k], alpha=.3)
#plt.title('Sorting random separable permutations', fontsize=25)
plt.xlabel('Locality parameter', fontsize=26)
plt.ylabel('Avg. number of operations / size', fontsize=26)
plt.xticks(fontsize=20)
......@@ -126,7 +117,6 @@ if __name__ == "__main__":
minCompsPerSize = []
sortedInput = []
#testInput = []
# ----------localised permutation inputs--------------
# randomness parameter: standard deviation
......
scripts/paper-sorting-sep.py
+ 1
− 4
View file @ ca19b8a5
......@@ -57,7 +57,6 @@ def plot_avg_counts(avgCounts):
plt.plot([2**p for p in range(4, MAXSIZE)], avgLinks[3:MAXSIZE-1], color=COLOURS[k], linestyle="--", marker=MARKERS_LINK[k], markerfacecolor=COLOURS[k], markersize=9, markeredgewidth=1, markeredgecolor='black', label=TYPES[k] + " links")
plt.fill_between([2**p for p in range(4, MAXSIZE)], minLinks[3:MAXSIZE-1], maxLinks[3:MAXSIZE-1], color=SHADE_COLOURS[k], alpha=.3)
#plt.title('Sorting random separable permutations', fontsize=25)
plt.xlabel('Input size', fontsize=26)
plt.ylabel('Avg. number of operations / size', fontsize=26)
plt.xticks(fontsize=20)
......@@ -131,7 +130,6 @@ if __name__ == "__main__":
minCompsPerSize = []
sortedInput = []
# testInput = []
# ----------separable permutation---------------------
# parameter: length (must be power of two)
......@@ -147,7 +145,6 @@ if __name__ == "__main__":
minCountsComps = [1000000000000 for _ in range(MAX_TYPE_KEY + 1)]
for zz in range(NUMBER_TESTS):
testInput = copy.copy(sortedInput)
testInput = separablePermutation(x)
testInput[0] = -1
for heapType in TYPES.keys():
......
scripts/paper-sorting-subseq.py
+ 1
− 6
View file @ ca19b8a5
......@@ -23,12 +23,9 @@ from pairing_heap import PairingHeap
COUNT_TYPE_BOTH = 0
COUNT_TYPE_LINKS = -1
COUNT_TYPE_COMPS = -2
MAXSIZE = 17
NUMBER_TESTS = 20 # number of tests to run
TEST_SIZES = [j for j in range(MAXSIZE)]
LIST_LEN = 10000 # number of elements in test list
TEST_SIZE = 10000 # number of elements in test list
STEP_SIZE = 100
INCREMENT_SUBSEQS = 100
TYPES = {0: "Pairing", 12: "Smooth"}
MAX_TYPE_KEY = max(TYPES.keys())
......@@ -129,7 +126,6 @@ if __name__ == "__main__":
minCompsPerSize = []
sortedInput = []
# testInput = []
# ----------continuous sorted subsequences inputs-----
# randomness parameter: subsequence lengths
params = [fac*INCREMENT_SUBSEQS for fac in range(1, math.ceil((TEST_SIZE/5)/INCREMENT_SUBSEQS), 1)]
......@@ -146,7 +142,6 @@ if __name__ == "__main__":
for zz in range(NUMBER_TESTS):
testInput = generateContSortedSubseq(sortedInput, x)
#print(len(testInput))
for heapType in TYPES.keys():
linkCount = 0
compCount = 0
......
scripts/paper-sorting-uniform.py
+ 3
− 8
View file @ ca19b8a5
......@@ -8,8 +8,6 @@ from random import shuffle
import matplotlib.pyplot as plt
import copy
import csv
from node import Node
from pairing_heap import PairingHeap
import os, sys, inspect
......@@ -17,17 +15,14 @@ import os, sys, inspect
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
from node import Node
from pairing_heap import PairingHeap
COUNT_TYPE_BOTH = 0
COUNT_TYPE_LINKS = -1
COUNT_TYPE_COMPS = -2
MAXSIZE = 18
NUMBER_TESTS = 5 # number of tests to run
TEST_SIZES = [j for j in range(MAXSIZE)] #
LIST_LEN = 10000 # number of elements in test list
TEST_SIZE = 10000 # number of elements in test list
STEP_SIZE = 100
INCREMENT_LOC = 0.005
INCREMENT_SUBSEQS = 100
TYPES = {0: "Pairing", 12: "Smooth"}
MAX_TYPE_KEY = max(TYPES.keys())
COLOURS = {0: 'xkcd:fire engine red', 12: 'xkcd:green'}
......
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