def call_linear_search(value_sought):
"""Call the iterative version of the binary search"""
# We need to do make a subroutine call in the scope of time_searches so we can
# pass the global variable corpus. corpus is out of scope of the actual
# binary search routine, so we have to pass it (it gets passed by reference,
# which is fast)
linear_search.linear_search(corpus, value_sought)
def test_linear_search_raises_an_error_with_an_invalid_target(self):
target = 1
try:
linear_search(self.unsorted_values, target)
except ValueError as e:
self.assertEqual(e.message,
"{} was not found in the list".format(target))
def test_linear_search(self):
arr1 = [-2, 7, 3, -9, 5, 1, 0, 4, -6]
arr2 = []
self.assertEqual(linear_search(arr1, 6), -1)
self.assertEqual(linear_search(arr1, -6), 8)
self.assertEqual(linear_search(arr1, 0), 6)
self.assertEqual(linear_search(arr2, 3), -1)
def min2d_yielded(self): x = self.initial_coordinates while self.norm(self.g(x)) > self.eps: ls = linear_search(x, self.f, self.g(x), self.eps) p_0 = ls.min() ls = linear_search(p_0, self.f, self.g(p_0), self.eps) p_1 = ls.min() d = map(op.sub, p_1, x) ls = linear_search(x, self.f, d, self.eps) x = ls.min() yield p_0, p_1, x
def dotest(terms, expected, which):
files = filelist(rootdir)
terms = words(terms)
# print(terms)
if which == 0:
linear_docs = linear_search(files, terms)
# print(filenames(linear_docs))
names = filenames(linear_docs)
names.sort()
expected.sort()
#assert filenames(linear_docs) == expected
assert names == expected, "found "+str(names)+" != expected "+str(expected)
elif which == 1:
index = create_index(files)
index_docs = index_search(files, index, terms)
# print(filenames(index_docs))
names = filenames(index_docs)
names.sort()
expected.sort()
#assert filenames(index_docs) == expected
assert names == expected, "found "+str(names)+" != expected "+str(expected)
else:
index = myhtable_create_index(files)
index_docs = myhtable_index_search(files, index, terms)
# print(filenames(index_docs))
names = filenames(index_docs)
names.sort()
expected.sort()
#assert filenames(index_docs) == expected
assert names == expected, "found "+str(names)+" != expected "+str(expected)
def dotest(terms, expected, which):
files = filelist(rootdir)
terms = words(terms)
# print(terms)
if which == 0:
linear_docs = linear_search(files, terms)
# print(filenames(linear_docs))
names = filenames(linear_docs)
names.sort()
expected.sort()
#assert filenames(linear_docs) == expected
assert names == expected, "found "+str(names)+" != expected "+str(expected)
elif which == 1:
index = create_index(files)
index_docs = index_search(files, index, terms)
# print(filenames(index_docs))
names = filenames(index_docs)
names.sort()
expected.sort()
#assert filenames(index_docs) == expected
assert names == expected, "found "+str(names)+" != expected "+str(expected)
else:
index = myhtable_create_index(files)
index_docs = myhtable_index_search(files, index, terms)
# print(filenames(index_docs))
names = filenames(index_docs)
names.sort()
expected.sort()
#assert filenames(index_docs) == expected
assert names == expected, "found "+str(names)+" != expected "+str(expected)
def time_check(choice: str, ite: int):
l = []
lim = 10000000
if choice == 'sorted':
for i in range(lim):
l.append(random.randint(0, lim))
l.sort()
elif choice == 'normalized':
for i in range(lim):
l.append(i)
inter = []
seq = []
bnry = []
indexed = []
for i in range(0, ite):
value = random.randint(0, lim)
if (choice == 'normalized'):
start2 = time.clock()
ret1 = interpolation_search(l, value)
end2 = time.clock()
if ret1 == True:
time2 = end2 - start2
inter.append(time2)
start1 = time.clock()
ret2 = linear_search(l, value)
end1 = time.clock()
if ret2 == True:
time1 = end1 - start1
seq.append(time1)
start3 = time.clock()
ret3 = binary_search(l, value)
end3 = time.clock()
if ret3 == True:
time3 = end3 - start3
bnry.append(time3)
start4 = time.clock()
ret4 = index_search(l, value)
end4 = time.clock()
if ret4 == True:
time4 = end4 - start4
indexed.append(time4)
plt.xlabel('Iterations')
plt.ylabel('Time')
if len(inter) != 0:
plt.plot(inter, color='red', label='interpolation')
plt.plot(seq, color='green', label='sequential')
plt.plot(bnry, color='blue', label='binary')
plt.plot(indexed, color='purple', label='indexed')
plt.legend()
plt.grid(True)
plt.show()
def main():
'''Main Function able to run all algorithms present in List_Algorithms.txt '''
file = open('List_Algorithms.txt', "r")
lines = list(file)
for line in lines:
print(line.strip('\n'))
file.close()
algorithm_to_call = int(
input("Enter the number from below list algorithms :\n"))
#PRIME CHECK CALL
if algorithm_to_call == 1:
print("You Have Selected PrimeCheck")
from prime_check_file import prime_check
if prime_check(int(
input("Enter a number to check Prime/Not Prime :"))):
print("It is a Prime Number")
else:
print("Not a Prime Number!")
#Binary_Search_recursive algorithm_to_call
if algorithm_to_call == 2:
from binary_search_recursive import binary_search_rec
print("You Have Selected Binary_Search_recursive algorithm")
elements = [
int(i) for i in input(
"Enter List of number in Ascending Order\n").split()
]
item = int(input("Enter Item you want to Search :\n"))
print("[Recursive Binary search function called]")
print(binary_search_rec(item, elements, 0, len(elements), 0))
#Binary_Search_iterative algorithm_to_call
if algorithm_to_call == 3:
from binary_search_iterative import binary_search_iter
print("You Have Selected Binary_Search_iterative algorithm")
elements = [
int(i) for i in input(
"Enter List of number in Ascending Order\n").split()
]
item = int(input("Enter Item you want to Search :\n"))
print(binary_search_iter(item, elements, 0, len(elements), 0))
#Linear Search algorithm_to_call
if algorithm_to_call == 4:
from linear_search import linear_search
print("You Have Selected linear search algorithm")
elements = [int(i) for i in input("Enter List of numbers\n").split()]
item = int(input("Enter number you want to search :\n"))
print(linear_search(elements, item))
#Jump Search algorithm_to_call
if algorithm_to_call == 5:
import math
from jump_search import jump_search
print("You Have Selected jump search algorithm")
elements = [int(i) for i in input("Enter List of numbers\n").split()]
item = int(input("Enter number you want to search :\n"))
print(jump_search(elements, item, 0, int(math.sqrt(len(elements))), 0))
def min(self, error = None): if error == None: error = lambda _x: self.norm(self.g(_x)) x = self.initial_coordinates while error(x) > self.eps: ls = linear_search(x, self.f, self.g(x), self.eps) p_0, eta_4 = ls.min() ls = linear_search(p_0, self.f, self.g(p_0), self.eps) p_1, eta_4 = ls.min() d = map(op.sub, p_1, x) ls = linear_search(x, self.f, d, self.eps) x, eta_4 = ls.min() print 'x =', x return x
def tester():
item = 20
list_one = [10,20,40,50,56,2,4,5,7,8,10,120,67,45,
24,27,62,65,78,28,77,69,65,58,66,88,100,73,88,16,
39,58,68,49,59,72,50,76,6782,36,100,22,17,81,70,42,
88,85,23,25,42,41,83,43,60,75,46,79,12,18,6,43,
17,42,56,89,50,94,72,68,43,82,53,82,11,94,63,74,67,
65,77,96,95,61,85,9,26,91,91,44,83,7,90,71,37,73,85,14]
print "Number {} is found at position : {}".format(item,linear_search(list_one,item))
def test_linear_berlitz_none():
terms = "missspellinnng"
files = filelist(rootdir)
terms = words(terms)
linear_docs = linear_search(files, terms)
expected = []
assert filenames(linear_docs)==expected
def test_linear_berlitz():
terms = "hawaii travel"
files = filelist(rootdir)
terms = words(terms)
linear_docs = linear_search(files, terms)
expected = ['HistoryHawaii.txt']
assert filenames(linear_docs)==expected
def test_linear_berlitz():
terms = "hawaii travel"
files = filelist(rootdir)
terms = words(terms)
linear_docs = linear_search(files, terms)
expected = ['HistoryHawaii.txt']
assert filenames(linear_docs) == expected
def test_linear_berlitz_none():
terms = "missspellinnng"
files = filelist(rootdir)
terms = words(terms)
linear_docs = linear_search(files, terms)
expected = []
assert filenames(linear_docs) == expected
def time_search_algorithms(list_length=1, generator=random_list):
# generate a sample list and search_item
search_list = generator(list_length=list_length)
search_item = random.randint(0, 10000)
# dictionary for storing values
times = {}
# keep track of list length
times['length'] = list_length
start_time = timeit.default_timer()
linear_search(search_list, search_item)
duration = timeit.default_timer() - start_time
times['linear'] = duration
start_time = timeit.default_timer()
binary_search(sorted(search_list), search_item)
duration = timeit.default_timer() - start_time
times['binary'] = duration
return times
def test_linear_search():
assert linear_search([1, 2, 3, 4, 5], 2) == 1
assert linear_search([1, 2, 3, 4, 5], -4) == -1
assert linear_search([], 0) == -1
assert linear_search([9, 9, 9], 9) == 0
assert linear_search([-9, 2, 5, -1, 11432, 2, 6], 11432) == 4
assert linear_search([-9, 2, 5, -1, 11432, 2, 6], 6) == 6
def plotComplexityLinear(self):
random_values = random.sample(range(1000000), 1000000)
sortedX = sorted(random_values)
time_taken = {}
step_size = 10000
for _ in range(int(len(sortedX) / step_size)):
start_time = time()
x = linear_search(sortedX, sortedX[-1])
#x=binary_search(sortedX,sortedX[-1])
end_time = time()
time_taken[len(sortedX)] = end_time - start_time
sortedX = sortedX[step_size:]
return time_taken
def fit(self, start_points):
start_points = np.array(start_points)
assert len(start_points) == len(self.X_)
result = [start_points]
points = copy.deepcopy(start_points)
points = np.array(points, np.double)
kv = lambda x : {var : val for var, val in zip(self.X_, x)}
dx = lambda x : np.array([dfx.subs(kv(x)) for dfx in self.DFX_], np.double)
for i in range(1000):
d = dx(points)
if np.linalg.norm(d) < self.threshold_:
break
d = self.norm(d)
alpha = linear_search(points, self.X_, self.FX_, d, self.threshold_ / 10)
points = points + alpha * d
result.append(points)
return result
def fit(self, start_values):
start_values = np.array(start_values)
assert len(start_values) == len(self.X_)
result = [start_values]
x = copy.deepcopy(start_values)
x = np.array(x, np.double)
d = 0
alpha = 0
for i in range(1000):
d = -self.dx(x) + alpha * d
if np.linalg.norm(d) < self.threshold_:
break
l = linear_search(x, self.X_, self.FX_, d, self.threshold_ / 10)
x0 = np.copy(x)
x = x + l * d
result.append(x)
alpha = self.alpha_func_(self.dx, x0, x)
return result
def test_linear_search_returns_an_index(self):
position = linear_search(self.unsorted_values, self.target)
assert position in range(self.items_length)
assert isinstance(position, int)
def test_linear_search_finds_target(self):
position = linear_search(self.unsorted_values, self.target)
self.assertEqual(self.unsorted_values[position], self.target)
import linear_search # type: ignore
import binary_search # type: ignore
import random
if __name__ == '__main__':
list_size = int(input('List size: '))
list = sorted([random.randint(0, 100) for i in range(list_size)])
# print(list)
goal = int(input('Which number do you want to find?: '))
print('-----')
print('LINEAR SEARCH:')
(found, iter_lin) = linear_search.linear_search(list, goal)
print(f'It took {iter_lin} iterations')
print(f'The goal element {goal} {"is" if found else "is not"} in the list')
print('-----')
print('BINARY SEARCH:')
(found, iter_bin) = binary_search.binary_search(list, 0,
len(list) - 1, goal)
print(f'It took {iter_bin} iterations')
print(f'The goal element {goal} {"is" if found else "is not"} in the list')
def test_valid_neg_input():
result = linear_search(1000, 5120)
assert result == False
def test_linear_search_finds_target(self):
position = linear_search(self.unsorted_values, self.target)
self.assertEqual(self.unsorted_values[position], self.target)
import ternary_search as ts import binary_search as bs import linear_search as ls l1 = [16, 20, 26, 30, 36, 40, 46, 50, 56, 60, 66, 70, 76, 80] target = 76 ternary_search = ts.ternary_search(l1, target) ternary_search.ternary_search() binary_search = bs.binary_search(l1, target) binary_search.binary_search() linear_search = ls.linear_search(l1, target) linear_search.linear_search()
def test_general_case(self):
arr = [1, 6, 3, 8, 9, 5]
val = 8
self.assertEqual(linear_search(arr, val), 3)
def test_linear_search_returns_an_index(self):
position = linear_search(self.unsorted_values, self.target)
assert position in range(self.items_length)
assert isinstance(position, int)
def test_linear_search_did_not_find_target(): iterable = ["apple", "grape", "orange"] assert linear_search(iterable, "banana") == False
def test_linear_search_find_target(): iterable = ["apple", "grape", "orange"] assert linear_search(iterable, "grape") == True
def test_not_in_the_list(self):
items = [1, 2, 3, 4, 5, 6, 7, 8, 9]
x = 0
self.assertEqual(None, linear_search(items, x))
def test_first_item(self):
items = [1, 2, 3, 4, 5, 6, 7, 8, 9]
x = 1
self.assertEqual(0, linear_search(items, x))
def test_simple(self):
items = [1, 2, 3, 4, 5, 6, 7, 8, 9]
x = 5
self.assertEqual(4, linear_search(items, x))
def test_unsorted(self):
items = [1, 5, 9, 4, 2, 6, 7, 8, 3]
x = 6
self.assertEqual(5, linear_search(items, x))
from time import time
import random
from linear_search import linear_search
random_values = random.sample(range(1000000), 1000000)
sortedX = sorted(random_values)
start_time = time()
linear_search(random_values, 999999)
end_time = time()
time_taken = end_time - start_time
print("Time taken for the operation is ", time_taken)
from linear_search import linear_search print(linear_search(3, [1, 2, 3]) == 2) print(linear_search(4, [1, 2, 3]) == None) print(linear_search(13, [1, 2, 3]) == None)
import random
import matplotlib.pyplot as plt
from linear_search import linear_search
from timeit import default_timer as timer
data_size = list()
best_case = list()
worst_case = list()
for i in range(1000, 100001, 500):
data = random.sample(range(100000), i)
data_size.append(i)
start_time_worst = timer()
linear_search(data, data[-1])
worst_case.append(timer() - start_time_worst)
start_time_best = timer()
linear_search(data, data[0])
best_case.append(timer() - start_time_best)
plt.plot(data_size, best_case, 'g', label="Best Case")
plt.plot(data_size, worst_case, 'r', label="Worst Case")
plt.xlabel("Data Size")
plt.ylabel("Time Taken")
plt.title("Best and Worst case Time Complexity of Linear Search")
plt.legend()
plt.savefig('Linear Search.png', dpi=300, format='png', pad_inches=1.0)
plt.show()
def test_linear_search_raises_an_error_with_an_invalid_target(self):
target = 1
try:
linear_search(self.unsorted_values, target)
except ValueError as e:
self.assertEqual(e.message, "{} was not found in the list".format(target))
impl = sys.argv[1]
rootdir = sys.argv[2]
files = filelist(rootdir)
# Uncomment the next line to test just the first 100 files instead of all files
# files = files[:100]
N = len(files)
print(N, "files")
index = None
while True:
terms = input("Search terms: ")
terms = words(terms)
if impl=='linear':
docs = linear_search(files, terms)
elif impl == 'index':
if index is None:
index = create_index(files)
print("Index complete")
docs = index_search(files, index, terms)
elif impl == 'myhtable':
if index is None:
index = myhtable_create_index(files)
print("Index complete")
docs = myhtable_index_search(files, index, terms)
else:
print("Invalid search type:", impl)
break
page = results(docs, terms)
with open("/tmp/results.html", "w", encoding='UTF-8') as f:
def test_element_not_in_list(self):
arr = [1, 6, 3, 8, 9, 5]
val = 2
self.assertIsNone(linear_search(arr, val))
def test_empty_list(self):
arr = []
val = 42
self.assertIsNone(linear_search(arr, val))
def test_singleton_list(self):
arr = [5]
val = 5
self.assertEqual(linear_search(arr, val), 0)
def test_search_first(self):
self.assertEqual(0, search.linear_search(self.my_list, 3))
def test_valid_pos_input():
result = linear_search(1000, 50)
assert result == True
def test_search_last(self):
self.assertEqual(6, search.linear_search(self.my_list, 78))
def test_invalid_input():
result = linear_search(0, 0)
def test_search_inexisting(self):
self.assertEqual(-1, search.linear_search(self.my_list, 4))
def test_last_element_is_value(self):
arr = [1, 6, 3, 8, 9, 5]
val = 5
self.assertEqual(linear_search(arr, val), 5)
def test_search_item(self):
self.assertEqual(2, search.linear_search(self.my_list, 12))
def TestPassed(self):
self.assertAlmostEqual(ls.linear_search(-(math.pi / 2), math.pi / 2),
-1.57079600431)
print("OK.")
def test_last_item(self):
items = [1, 2, 3, 4, 5, 6, 7, 8, 9]
x = 9
self.assertEqual(8, linear_search(items, x))
impl = sys.argv[1]
rootdir = sys.argv[2]
files = filelist(rootdir)
# Uncomment the next line to test just the first 100 files instead of all files
# files = files[:100]
N = len(files)
print N, "files"
index = None
while True:
terms = raw_input("Search terms: ")
terms = words(terms)
if impl == 'linear':
docs = linear_search(files, terms)
elif impl == 'index':
if index is None:
index = create_index(files)
print "Index complete"
docs = index_search(files, index, terms)
elif impl == 'myhtable':
if index is None:
index = myhtable_create_index(files)
print "Index complete"
docs = myhtable_index_search(files, index, terms)
else:
print "Invalid search type:", impl
break
page = results(docs, terms)
f = open("/tmp/results.html", "w")
def test_search(self):
data = [1, 2, 3, 5, 6, 12, 7, 4, 8]
self.assertEqual(linear_search(data, 6), 4)
self.assertEqual(linear_search(data, 10), -1)
