def __new__(cls, elements=None, heap_property="min", d=4):
obj = Heap.__new__(cls)
obj.heap_property = heap_property
obj.d = d
if heap_property == "min":
obj._comp = lambda key_parent, key_child: key_parent <= key_child
elif heap_property == "max":
obj._comp = lambda key_parent, key_child: key_parent >= key_child
else:
raise ValueError("%s is invalid heap property" % (heap_property))
if elements is None:
elements = DynamicOneDimensionalArray(TreeNode, 0)
elif _check_type(elements, (list, tuple)):
elements = DynamicOneDimensionalArray(TreeNode, len(elements),
elements)
elif _check_type(elements, Array):
elements = DynamicOneDimensionalArray(TreeNode, len(elements),
elements._data)
else:
raise ValueError(
f'Expected a list/tuple/Array of TreeNode got {type(elements)}'
)
obj.heap = elements
obj._last_pos_filled = obj.heap._last_pos_filled
obj._build()
return obj
def __new__(cls, key, data=None):
from pydatastructs.linear_data_structures.arrays import DynamicOneDimensionalArray
obj = Node.__new__(cls)
obj.data, obj.key = data, key
obj.children, obj.parent, obj.is_root = (DynamicOneDimensionalArray(
BinomialTreeNode, 0), None, False)
return obj
def __new__(cls, root_list=[]):
if not all((_check_type(root, BinomialTree)) for root in root_list):
raise TypeError("The root_list should contain "
"references to objects of BinomialTree.")
obj = Heap.__new__(cls)
obj.root_list = DynamicOneDimensionalArray(BinomialTree, root_list)
return obj
def __new__(cls, key, data=None):
from pydatastructs.linear_data_structures.arrays import DynamicOneDimensionalArray
obj = Node.__new__(cls)
obj.data = data
obj.key = key
obj.is_root = False
obj.children = DynamicOneDimensionalArray(int, 0)
return obj
def __new__(cls, key, data=None, **kwargs):
raise_if_backend_is_not_python(cls,
kwargs.get('backend', Backend.PYTHON))
from pydatastructs.linear_data_structures.arrays import DynamicOneDimensionalArray
obj = Node.__new__(cls)
obj.data, obj.key = data, key
obj.children, obj.parent, obj.is_root = (DynamicOneDimensionalArray(
BinomialTreeNode, 0), None, False)
return obj
def _do_match(string, query, kmp_table):
j, k = 0, 0
positions = DynamicOneDimensionalArray(int, 0)
while j < len(string):
if query[k] == string[j]:
j = j + 1
k = k + 1
if k == len(query):
positions.append(j - k)
k = kmp_table[k]
else:
k = kmp_table[k]
if k < 0:
j = j + 1
k = k + 1
return positions
def __new__(cls, key, data=None, **kwargs):
raise_if_backend_is_not_python(cls,
kwargs.get('backend', Backend.PYTHON))
from pydatastructs.linear_data_structures.arrays import DynamicOneDimensionalArray
obj = Node.__new__(cls)
obj.data = data
obj.key = key
obj.is_root = False
obj.children = DynamicOneDimensionalArray(int, 0)
return obj
def _rabin_karp(text, query):
t = len(text)
q = len(query)
positions = DynamicOneDimensionalArray(int, 0)
if q == 0 or t == 0:
return positions
query_hash = _hash_str(query)
text_hash = OneDimensionalArray(int, t + 1)
text_hash.fill(0)
p_pow = _p_pow(t)
for i in range(t):
text_hash[i + 1] = (text_hash[i] + ord(text[i]) * p_pow[i]) % MOD
for i in range(t - q + 1):
curr_hash = (text_hash[i + q] + MOD - text_hash[i]) % MOD
if curr_hash == (query_hash * p_pow[i]) % MOD:
positions.append(i)
return positions
def test_TernaryHeap():
max_heap = TernaryHeap(heap_property="max")
assert raises(IndexError, lambda: max_heap.extract())
max_heap.insert(100, 100)
max_heap.insert(19, 19)
max_heap.insert(36, 36)
max_heap.insert(17, 17)
max_heap.insert(3, 3)
max_heap.insert(25, 25)
max_heap.insert(1, 1)
max_heap.insert(2, 2)
max_heap.insert(7, 7)
assert str(max_heap) == \
('[(100, 100, [1, 2, 3]), (25, 25, [4, 5, 6]), '
'(36, 36, [7, 8]), (17, 17, []), '
'(3, 3, []), (19, 19, []), (1, 1, []), '
'(2, 2, []), (7, 7, [])]')
assert max_heap.extract().key == 100
expected_sorted_elements = [36, 25, 19, 17, 7, 3, 2, 1]
sorted_elements = []
for _ in range(8):
sorted_elements.append(max_heap.extract().key)
assert expected_sorted_elements == sorted_elements
elements = [
TreeNode(7, 7),
TreeNode(25, 25),
TreeNode(100, 100),
TreeNode(1, 1),
TreeNode(2, 2),
TreeNode(3, 3),
TreeNode(17, 17),
TreeNode(19, 19),
TreeNode(36, 36)
]
min_heap = TernaryHeap(elements=DynamicOneDimensionalArray(
TreeNode, 9, elements),
heap_property="min")
expected_extracted_element = min_heap.heap[0].key
assert min_heap.extract().key == expected_extracted_element
expected_sorted_elements = [2, 3, 7, 17, 19, 25, 36, 100]
sorted_elements = [min_heap.extract().key for _ in range(8)]
assert expected_sorted_elements == sorted_elements
def test_DHeap():
assert raises(ValueError, lambda: DHeap(heap_property="none", d=4))
max_heap = DHeap(heap_property="max", d=5)
assert max_heap.extract() is None
max_heap.insert(100, 100)
max_heap.insert(19, 19)
max_heap.insert(36, 36)
max_heap.insert(17, 17)
max_heap.insert(3, 3)
max_heap.insert(25, 25)
max_heap.insert(1, 1)
max_heap = DHeap(max_heap.heap, heap_property="max", d=4)
max_heap.insert(2, 2)
max_heap.insert(7, 7)
assert str(max_heap) == \
('[(100, 100, [1, 2, 3, 4]), (25, 25, [5, 6, 7, 8]), '
'(36, 36, []), (17, 17, []), (3, 3, []), (19, 19, []), '
'(1, 1, []), (2, 2, []), (7, 7, [])]')
assert max_heap.extract().key == 100
expected_sorted_elements = [36, 25, 19, 17, 7, 3, 2, 1]
sorted_elements = []
for _ in range(8):
sorted_elements.append(max_heap.extract().key)
assert expected_sorted_elements == sorted_elements
elements = [
TreeNode(7, 7),
TreeNode(25, 25),
TreeNode(100, 100),
TreeNode(1, 1),
TreeNode(2, 2),
TreeNode(3, 3),
TreeNode(17, 17),
TreeNode(19, 19),
TreeNode(36, 36)
]
min_heap = DHeap(elements=DynamicOneDimensionalArray(
TreeNode, 9, elements),
heap_property="min")
assert min_heap.extract().key == 1
expected_sorted_elements = [2, 3, 7, 17, 19, 25, 36, 100]
sorted_elements = [min_heap.extract().key for _ in range(8)]
assert expected_sorted_elements == sorted_elements
def merge(self, other_heap):
"""
Merges current binomial heap with the given binomial heap.
Parameters
==========
other_heap: BinomialHeap
"""
if not _check_type(other_heap, BinomialHeap):
raise TypeError("Other heap is not of type BinomialHeap.")
new_root_list = DynamicOneDimensionalArray(BinomialTree, 0)
i, j = 0, 0
while ((i <= self.root_list._last_pos_filled)
and (j <= other_heap.root_list._last_pos_filled)):
new_tree = None
while self.root_list[i] is None:
i += 1
while other_heap.root_list[j] is None:
j += 1
if self.root_list[i].order == other_heap.root_list[j].order:
new_tree = self.merge_tree(self.root_list[i],
other_heap.root_list[j])
i += 1
j += 1
else:
if self.root_list[i].order < other_heap.root_list[j].order:
new_tree = self.root_list[i]
i += 1
else:
new_tree = other_heap.root_list[j]
j += 1
self._merge_heap_last_new_tree(new_root_list, new_tree)
while i <= self.root_list._last_pos_filled:
new_tree = self.root_list[i]
self._merge_heap_last_new_tree(new_root_list, new_tree)
i += 1
while j <= other_heap.root_list._last_pos_filled:
new_tree = other_heap.root_list[j]
self._merge_heap_last_new_tree(new_root_list, new_tree)
j += 1
self.root_list = new_root_list
def __new__(cls, elements=None, heap_property="min", d=4):
obj = Heap.__new__(cls)
obj.heap_property = heap_property
obj.d = d
if heap_property == "min":
obj._comp = lambda key_parent, key_child: key_parent <= key_child
elif heap_property == "max":
obj._comp = lambda key_parent, key_child: key_parent >= key_child
else:
raise ValueError("%s is invalid heap property" % (heap_property))
if elements is None:
elements = DynamicOneDimensionalArray(TreeNode, 0)
else:
if not all(map(lambda x: _check_type(x, TreeNode), elements)):
raise ValueError("Expect a list/tuple of TreeNode got %s" %
(elements))
obj.heap = elements
obj._last_pos_filled = obj.heap._last_pos_filled
obj._build()
return obj
def test_BinaryHeap():
max_heap = BinaryHeap(heap_property="max")
assert raises(IndexError, lambda: max_heap.extract())
max_heap.insert(100, 100)
max_heap.insert(19, 19)
max_heap.insert(36, 36)
max_heap.insert(17, 17)
max_heap.insert(3, 3)
max_heap.insert(25, 25)
max_heap.insert(1, 1)
max_heap.insert(2, 2)
max_heap.insert(7, 7)
assert str(max_heap) == \
("[(100, 100, [1, 2]), (19, 19, [3, 4]), "
"(36, 36, [5, 6]), (17, 17, [7, 8]), "
"(3, 3, []), (25, 25, []), (1, 1, []), "
"(2, 2, []), (7, 7, [])]")
assert max_heap.extract().key == 100
expected_sorted_elements = [36, 25, 19, 17, 7, 3, 2, 1]
l = max_heap.heap[0].left
l = max_heap.heap[0].right
sorted_elements = []
for _ in range(8):
sorted_elements.append(max_heap.extract().key)
assert expected_sorted_elements == sorted_elements
elements = [
TreeNode(7, 7),
TreeNode(25, 25),
TreeNode(100, 100),
TreeNode(1, 1),
TreeNode(2, 2),
TreeNode(3, 3),
TreeNode(17, 17),
TreeNode(19, 19),
TreeNode(36, 36)
]
min_heap = BinaryHeap(elements=DynamicOneDimensionalArray(
TreeNode, 9, elements),
heap_property="min")
assert min_heap.extract().key == 1
expected_sorted_elements = [2, 3, 7, 17, 19, 25, 36, 100]
sorted_elements = [min_heap.extract().key for _ in range(8)]
assert expected_sorted_elements == sorted_elements
non_TreeNode_elements = [(7, 7),
TreeNode(25, 25),
TreeNode(100, 100),
TreeNode(1, 1), (2, 2),
TreeNode(3, 3),
TreeNode(17, 17),
TreeNode(19, 19),
TreeNode(36, 36)]
assert raises(
ValueError, lambda: BinaryHeap(elements=non_TreeNode_elements,
heap_property='min'))
def longest_increasing_subsequence(array, **kwargs):
"""
Returns the longest increasing subsequence (as a OneDimensionalArray) that
can be obtained from a given OneDimensionalArray. A subsequence
of an array is an ordered subset of the array's elements having the same
sequential ordering as the original array. Here, an increasing
sequence stands for a strictly increasing sequence of numbers.
Parameters
==========
array: OneDimensionalArray
The given array in the form of a OneDimensionalArray
backend: pydatastructs.Backend
The backend to be used.
Optional, by default, the best available
backend is used.
Returns
=======
output: OneDimensionalArray
Returns the longest increasing subsequence that can be obtained
from the given array
Examples
========
>>> from pydatastructs import lower_bound, OneDimensionalArray as ODA
>>> from pydatastructs import longest_increasing_subsequence as LIS
>>> array = ODA(int, [2, 5, 3, 7, 11, 8, 10, 13, 6])
>>> longest_inc_subsequence = LIS(array)
>>> str(longest_inc_subsequence)
'[2, 3, 7, 8, 10, 13]'
>>> array2 = ODA(int, [3, 4, -1, 5, 8, 2, 2 ,2, 3, 12, 7, 9, 10])
>>> longest_inc_subsequence = LIS(array2)
>>> str(longest_inc_subsequence)
'[-1, 2, 3, 7, 9, 10]'
"""
raise_if_backend_is_not_python(longest_increasing_subsequence,
kwargs.get('backend', Backend.PYTHON))
n = len(array)
dp = OneDimensionalArray(int, n)
dp.fill(0)
parent = OneDimensionalArray(int, n)
parent.fill(-1)
length = 0
for i in range(1, n):
if array[i] <= array[dp[0]]:
dp[0] = i
elif array[dp[length]] < array[i]:
length += 1
dp[length] = i
parent[i] = dp[length - 1]
else:
curr_array = [array[dp[i]] for i in range(length)]
ceil = lower_bound(curr_array, array[i])
dp[ceil] = i
parent[i] = dp[ceil - 1]
ans = DynamicOneDimensionalArray(int, 0)
last_index = dp[length]
while last_index != -1:
ans.append(array[last_index])
last_index = parent[last_index]
n = ans._last_pos_filled + 1
ans_ODA = OneDimensionalArray(int, n)
for i in range(n):
ans_ODA[n - 1 - i] = ans[i]
return ans_ODA
def _knuth_morris_pratt(text, query):
if len(text) == 0 or len(query) == 0:
return DynamicOneDimensionalArray(int, 0)
kmp_table = _build_kmp_table(query)
return _do_match(text, query, kmp_table)