def __init__(self,Q): self.coincidences = hashtable(Q * 10) self.arcsByPoint = hashtable(Q * 10) self.pointsByPoint = hashtable(Q * 10) self.arcs=[] self.forwardOut = [] self.backwardOut = []
def __init__(self, Q):
self.coincidences = hashtable(Q * 10)
self.arcsByPoint = hashtable(Q * 10)
self.pointsByPoint = hashtable(Q * 10)
self.arcs = []
self.forwardOut = []
self.backwardOut = []
def super_resolution_train(mat, Qangle, Qstrenth, Qcoherence):
Q = np.zeros((Qangle * Qstrenth * Qcoherence, 4, 11 * 11, 11 * 11))
V = np.zeros((Qangle * Qstrenth * Qcoherence, 4, 11 * 11, 1))
h = np.zeros((Qangle * Qstrenth * Qcoherence, 4, 11 * 11))
mat = cv2.cvtColor(mat, cv2.COLOR_BGR2YCrCb)[:, :, 2]
mat = cv2.normalize(mat.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)
HR = mat
LR = cv2.GaussianBlur(
HR, (0, 0), 2
) # should use low-resolution image but here use blur image because I am lazy
for xP in range(5, LR.shape[0] - 6):
for yP in range(5, LR.shape[1] - 6):
patch = LR[xP - 5:xP + 6, yP - 5:yP + 6]
[angle, strenth, coherence] = hashtable(patch, Qangle, Qstrenth,
Qcoherence)
j = angle * 9 + strenth * 3 + coherence
A = patch.reshape(1, -1)
b = HR[xP][yP]
t = xP % 2 * 2 + yP % 2
Q[j, t] += A * A.T
V[j, t] += A.T * b
for t in range(4):
for j in range(Qangle * Qstrenth * Qcoherence):
h[j, t] = cg(Q[j, t], V[j, t])[0]
return h
def testhash(data, hashtype = None):
h = hashtable.hashtable()
if hashtype is not None:
h.opentype = hashtype
for i in data:
h.insert(i, random.randint(0, 100))
print("=" * 30)
for i in h:
print(i)
print("=" * 30)
print(h.search(data[3]))
print(h.search(200))
print(h[data[7]])
h[101] = 45
print(h[1])
print(h.search(101))
print("=" * 30)
print(h.delete(data[3]))
print(h.delete(200))
print("=" * 30)
h.print()
print("=" * 30)
def rehash(self):
self.rehashes = self.rehashes + 1
old_hashtable = self.hashtable
self.tableSize = int(self.tableSize * self.rehashMultiplyer)
self.hashtable = hashtable.hashtable(self.tableSize)
for item in old_hashtable.table:
if isinstance(item, key.key):
self.rehashProbing(item, False)
def __init__(self, tab = None,
expander = taquin_expander_simple,
selector = taquin_selector_simple):
self.expander = expander
self.selector = selector
self.success = False
self.n = len(tab)
self.closed = hashtable()
self.e = (reduce(lambda x, y: x + y, tab, []), 0)
self.opened = hashtable()
self.opened.append(self.e)
self.goods = 0
for i in xrange(self.n ** 2):
if self.e[i] == i + 1:
self.goods += 1
else:
break
def __init__(self,
tab=None,
expander=taquin_expander_simple,
selector=taquin_selector_simple):
self.expander = expander
self.selector = selector
self.success = False
self.n = len(tab)
self.closed = hashtable()
self.e = (reduce(lambda x, y: x + y, tab, []), 0)
self.opened = hashtable()
self.opened.append(self.e)
self.goods = 0
for i in xrange(self.n**2):
if self.e[i] == i + 1:
self.goods += 1
else:
break
def __init__(self, tableSize, valueC=50, rehashMultiplyer=2):
self.ldown = 0
self.lup = 0
self.tableSize = tableSize
self.hashtable = hashtable.hashtable(tableSize)
self.valueC = valueC
self.rehashMultiplyer = rehashMultiplyer
self.numProbes = 0
self.rehashes = 0
self.maxCollisionChain = 0
self.collisionCounter = 0
self.curCollisionChain = 0
def test_plus_duplicate(self):
keys = set()
table2 = hashtable()
for i in range(1, self.CASES + 1):
self.table.add(chr(i), i)
table2.add(chr(i), self.CASES - i)
keys.add(chr(i))
self.table += table2
for key in self.table:
self.assertTrue(key in keys)
keys.remove(key)
self.assertEqual(self.CASES, self.table.size)
self.assertEqual(0, len(keys))
def testchainhash(data):
h = hashtable.hashtable()
for i in data:
h.chainedinsert(i, random.randint(0, 100))
print("=" * 30)
for i in h:
print(i)
print("=" * 30)
print(h.chainedsearch(data[3]))
print(h.chainedsearch(200))
print(h[data[7]])
h[101] = 4
print(h[data[7]])
print(h.chainedsearch(101))
print("=" * 30)
print(h.chaineddelete(data[3]))
print(h.chaineddelete(200))
print("=" * 30)
h.print()
print("=" * 30)
#! /usr/bin/env python
import hashlib
import hashtable
def calc_hash(word,hash_alg='md5'):
if hash_alg not in hashlib.algorithms:
print ("%s isn't exists in 'hashlib'!" % hash_alg)
return None
hash_obj = hashlib.new(hash_alg)
hash_obj.update(word.encode())
return hash_obj.hexdigest()
htable = hashtable.hashtable('htable-main.db')
print
try:
htable.createtable(hashlib.algorithms)
except:
pass
print
try:
with open('wordlist.txt','r') as file:
line = file.readline()
i = 1
while line != None and i < 10:
if line == '\n' or line == ' ':
line = file.readline()
def __init__(self,Q): self.coincidences = hashtable(Q * 10) self.arcsByPoint = hashtable(Q * 10) self.pointsByPoint = hashtable(Q * 10) self.arcs=[] self.length=0
elif width <= 2000 and height <= 2000:
fx = 1.3
fy = 1.3
else:
fx = 1
fy = 1
mat = cv2.imread(painting_name)
[width, height, channel] = mat.shape
h = np.load("lowR2.npy")
mat = cv2.cvtColor(mat, cv2.COLOR_BGR2YCrCb)[:, :, 2]
LR = cv2.resize(mat, (0, 0), fx=fx, fy=fy)
LRDirect = np.zeros((LR.shape[0], LR.shape[1]))
for xP in range(5, LR.shape[0] - 6):
for yP in range(5, LR.shape[1] - 6):
patch = LR[xP - 5:xP + 6, yP - 5:yP + 6]
[angle, strenth, coherence] = hashtable(patch, Qangle, Qstrenth,
Qcoherence)
j = angle * 9 + strenth * 3 + coherence
A = patch.reshape(1, -1)
t = xP % 2 * 2 + yP % 2
hh = np.matrix(h[j, t])
LRDirect[xP][yP] = hh * A.T
print("Test is off")
mat = cv2.imread(painting_name)
mat = cv2.cvtColor(mat, cv2.COLOR_BGR2YCrCb)
LR = cv2.resize(mat, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)
LRDirectImage = LR
LRDirectImage[:, :, 2] = LRDirect
A = cv2.cvtColor(LRDirectImage, cv2.COLOR_YCrCb2RGB)
im = Image.fromarray(A)
im.save(painting_name)
import hashtable
table = hashtable.hashtable(10)
links = ["https://reddit.com/r/futureporn" , "https://www.reddit.com/r/futureporn/top/?sort=top&t=all", "https://www.reddit.com/r/futureporn/top/?sort=top&t=all&count=25&after=t3_1wdbte" , "https://www.reddit.com/r/ImaginaryBestOf/top/?sort=top&t=all" , "https://www.reddit.com/r/ImaginaryBestOf/top/?sort=top&t=all&count=25&after=t3_3tk957" , "https://www.reddit.com/r/ImaginaryWinterscapes/top/?sort=top&t=all" , "https://www.reddit.com/user/Lol33ta/m/imaginarycharacters/top/?sort=top&t=all" , "https://www.reddit.com/user/Lol33ta/m/imaginarylandscapes/top/?sort=top&t=all"]
for link in links:
print table.visited(link), link
print table.visited("https://reddit.com/r/futureporn") , "https://reddit.com/r/futureporn"
table.printtable()
table.grow()
print "-------------new table-----------------------"
table.printtable()
import csv
from package import package
from hashtable import hashtable
import datetime
from datetime import time
import calculator
#Variables
hashtable = hashtable()
addressList = []
distanceList = []
#Read in package data from csv
with open('data/packages.csv') as packageFile:
reader = csv.reader(packageFile, delimiter=',')
for row in reader:
timestamp = time(hour=0, minute=0, second=0, microsecond=0)
temp = package(row[0], row[1], row[2], row[3], row[4], row[5], row[6],
row[7], timestamp)
hashtable.insert(int(temp.id) - 1, temp)
#Read in distance data from csv
with open('data/distances_table.csv') as distanceFile:
reader = csv.reader(distanceFile, delimiter=',')
for row in reader:
distanceList.append(row)
#Read in address data from csv
with open('data/addresses.csv') as addressFile:
reader = csv.reader(addressFile, delimiter=',')
picdirs = [PICS_PATH] for path in picdirs: try: os.makedirs(path) except OSError: if not os.path.isdir(path): raise if os.path.exists(BASE_PATH + "table.p"): table = pickle.load( open( "table.p", "rb" ) ) else: table = hashtable.hashtable(64) url = ["https://reddit.com/r/futureporn", "https://www.reddit.com/r/imaginarywinterscapes" , "https://www.reddit.com/r/ImaginaryMindscapes" , "https://www.reddit.com/r/ImaginaryWorlds" ] random.shuffle(url) count = 0 list = [] while( count < numberofpics): if len(list) < numberofpics - count: list = extract_pic_links(url , table , numberofpics * sources ) random.shuffle(list) sublist = []
def main(): ht = h.hashtable(10) print ht.hash(114)
def setUp(self):
self.table = hashtable()