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text stringlengths 75 104k |
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def encode(strs):
"""Encodes a list of strings to a single string.
:type strs: List[str]
:rtype: str
"""
res = ''
for string in strs.split():
res += str(len(string)) + ":" + string
return res |
def decode(s):
"""Decodes a single string to a list of strings.
:type s: str
:rtype: List[str]
"""
strs = []
i = 0
while i < len(s):
index = s.find(":", i)
size = int(s[i:index])
strs.append(s[index+1: index+1+size])
i = index+1+size
return strs |
def multiply(multiplicand: list, multiplier: list) -> list:
"""
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]]
"""
multiplicand_row, multiplicand_col = len(
multiplicand), len(multiplicand[0])
multiplier_row, multiplier_col = len(multiplier), len(multiplier... |
def combination(n, r):
"""This function calculates nCr."""
if n == r or r == 0:
return 1
else:
return combination(n-1, r-1) + combination(n-1, r) |
def combination_memo(n, r):
"""This function calculates nCr using memoization method."""
memo = {}
def recur(n, r):
if n == r or r == 0:
return 1
if (n, r) not in memo:
memo[(n, r)] = recur(n - 1, r - 1) + recur(n - 1, r)
return memo[(n, r)]
return recur(n... |
def is_anagram(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
maps = {}
mapt = {}
for i in s:
maps[i] = maps.get(i, 0) + 1
for i in t:
mapt[i] = mapt.get(i, 0) + 1
return maps == mapt |
def pancake_sort(arr):
"""
Pancake_sort
Sorting a given array
mutation of selection sort
reference: https://www.geeksforgeeks.org/pancake-sorting/
Overall time complexity : O(N^2)
"""
len_arr = len(arr)
if len_arr <= 1:
return arr
for cur in range(len(arr), 1, -1):... |
def next(self):
"""
:rtype: int
"""
v=self.queue.pop(0)
ret=v.pop(0)
if v: self.queue.append(v)
return ret |
def max_profit_naive(prices):
"""
:type prices: List[int]
:rtype: int
"""
max_so_far = 0
for i in range(0, len(prices) - 1):
for j in range(i + 1, len(prices)):
max_so_far = max(max_so_far, prices[j] - prices[i])
return max_so_far |
def max_profit_optimized(prices):
"""
input: [7, 1, 5, 3, 6, 4]
diff : [X, -6, 4, -2, 3, -2]
:type prices: List[int]
:rtype: int
"""
cur_max, max_so_far = 0, 0
for i in range(1, len(prices)):
cur_max = max(0, cur_max + prices[i] - prices[i-1])
max_so_far = max(max_so_far,... |
def first_unique_char(s):
"""
:type s: str
:rtype: int
"""
if (len(s) == 1):
return 0
ban = []
for i in range(len(s)):
if all(s[i] != s[k] for k in range(i + 1, len(s))) == True and s[i] not in ban:
return i
else:
ban.append(s[i])
return -1 |
def kth_smallest(self, root, k):
"""
:type root: TreeNode
:type k: int
:rtype: int
"""
count = []
self.helper(root, count)
return count[k-1] |
def int_to_roman(num):
"""
:type num: int
:rtype: str
"""
m = ["", "M", "MM", "MMM"];
c = ["", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM"];
x = ["", "X", "XX", "XXX", "XL", "L", "LX", "LXX", "LXXX", "XC"];
i = ["", "I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX"];
... |
def length_longest_path(input):
"""
:type input: str
:rtype: int
"""
curr_len, max_len = 0, 0 # running length and max length
stack = [] # keep track of the name length
for s in input.split('\n'):
print("---------")
print("<path>:", s)
depth = s.count('\t') #... |
def multiply(self, a, b):
"""
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]]
"""
if a is None or b is None: return None
m, n, l = len(a), len(b[0]), len(b[0])
if len(b) != n:
raise Exception("A's column number must be equal to B's row number.")
c = ... |
def multiply(self, a, b):
"""
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]]
"""
if a is None or b is None: return None
m, n = len(a), len(b[0])
if len(b) != n:
raise Exception("A's column number must be equal to B's row number.")
l = len(b[0])
... |
def bitonic_sort(arr, reverse=False):
"""
bitonic sort is sorting algorithm to use multiple process, but this code not containing parallel process
It can sort only array that sizes power of 2
It can sort array in both increasing order and decreasing order by giving argument true(increasing) and false(de... |
def scc(graph):
''' Computes the strongly connected components of a graph '''
order = []
vis = {vertex: False for vertex in graph}
graph_transposed = {vertex: [] for vertex in graph}
for (v, neighbours) in graph.iteritems():
for u in neighbours:
add_edge(graph_transposed, u, v)... |
def build_graph(formula):
''' Builds the implication graph from the formula '''
graph = {}
for clause in formula:
for (lit, _) in clause:
for neg in [False, True]:
graph[(lit, neg)] = []
for ((a_lit, a_neg), (b_lit, b_neg)) in formula:
add_edge(graph, (a_lit... |
def unique_array_sum_combinations(A, B, C, target):
"""
1. Sort all the arrays - a,b,c. - This improves average time complexity.
2. If c[i] < Sum, then look for Sum - c[i] in array a and b.
When pair found, insert c[i], a[j] & b[k] into the result list.
This can be done in O(n).
3. Keep on... |
def is_bst(root):
"""
:type root: TreeNode
:rtype: bool
"""
stack = []
pre = None
while root or stack:
while root:
stack.append(root)
root = root.left
root = stack.pop()
if pre and root.val <= pre.val:
return False
pre... |
def __get_depth(root):
"""
return 0 if unbalanced else depth + 1
"""
if root is None:
return 0
left = __get_depth(root.left)
right = __get_depth(root.right)
if abs(left-right) > 1 or -1 in [left, right]:
return -1
return 1 + max(left, right) |
def copy_random_pointer_v1(head):
"""
:type head: RandomListNode
:rtype: RandomListNode
"""
dic = dict()
m = n = head
while m:
dic[m] = RandomListNode(m.label)
m = m.next
while n:
dic[n].next = dic.get(n.next)
dic[n].random = dic.get(n.random)
n = ... |
def copy_random_pointer_v2(head):
"""
:type head: RandomListNode
:rtype: RandomListNode
"""
copy = defaultdict(lambda: RandomListNode(0))
copy[None] = None
node = head
while node:
copy[node].label = node.label
copy[node].next = copy[node.next]
copy[node].random = ... |
def get_factors(n):
"""[summary]
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors of the number n]
"""
def factor(n, i, combi, res):
"""[summary]
helper function
Arguments:
n {[int]} -- [number]
... |
def get_factors_iterative1(n):
"""[summary]
Computes all factors of n.
Translated the function get_factors(...) in
a call-stack modell.
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors]
"""
todo, res = [(n, 2, [])], []
while... |
def get_factors_iterative2(n):
"""[summary]
analog as above
Arguments:
n {[int]} -- [description]
Returns:
[list of lists] -- [all factors of n]
"""
ans, stack, x = [], [], 2
while True:
if x > n // x:
if not stack:
return ans
... |
def longest_increasing_subsequence(sequence):
"""
Dynamic Programming Algorithm for
counting the length of longest increasing subsequence
type sequence: List[int]
"""
length = len(sequence)
counts = [1 for _ in range(length)]
for i in range(1, length):
for j in range(0, i):
... |
def single_number3(nums):
"""
:type nums: List[int]
:rtype: List[int]
"""
# isolate a^b from pairs using XOR
ab = 0
for n in nums:
ab ^= n
# isolate right most bit from a^b
right_most = ab & (-ab)
# isolate a and b from a^b
a, b = 0, 0
for n in nums:
if ... |
def distance(x,y):
"""[summary]
HELPER-FUNCTION
calculates the (eulidean) distance between vector x and y.
Arguments:
x {[tuple]} -- [vector]
y {[tuple]} -- [vector]
"""
assert len(x) == len(y), "The vector must have same length"
result = ()
sum = 0
for i in range(le... |
def nearest_neighbor(x, tSet):
"""[summary]
Implements the nearest neighbor algorithm
Arguments:
x {[tupel]} -- [vector]
tSet {[dict]} -- [training set]
Returns:
[type] -- [result of the AND-function]
"""
assert isinstance(x, tuple) and isinstance(tSet, dict)
curren... |
def is_strobogrammatic(num):
"""
:type num: str
:rtype: bool
"""
comb = "00 11 88 69 96"
i = 0
j = len(num) - 1
while i <= j:
x = comb.find(num[i]+num[j])
if x == -1:
return False
i += 1
j -= 1
return True |
def merge_sort(arr):
""" Merge Sort
Complexity: O(n log(n))
"""
# Our recursive base case
if len(arr) <= 1:
return arr
mid = len(arr) // 2
# Perform merge_sort recursively on both halves
left, right = merge_sort(arr[:mid]), merge_sort(arr[mid:])
# Merge each side togethe... |
def merge(left, right, merged):
""" Merge helper
Complexity: O(n)
"""
left_cursor, right_cursor = 0, 0
while left_cursor < len(left) and right_cursor < len(right):
# Sort each one and place into the result
if left[left_cursor] <= right[right_cursor]:
merged[left_curs... |
def bucket_sort(arr):
''' Bucket Sort
Complexity: O(n^2)
The complexity is dominated by nextSort
'''
# The number of buckets and make buckets
num_buckets = len(arr)
buckets = [[] for bucket in range(num_buckets)]
# Assign values into bucket_sort
for value in arr:
inde... |
def k_closest(points, k, origin=(0, 0)):
# Time: O(k+(n-k)logk)
# Space: O(k)
"""Initialize max heap with first k points.
Python does not support a max heap; thus we can use the default min heap where the keys (distance) are negated.
"""
heap = [(-distance(p, origin), p) for p in points[:k]]
... |
def reverse_list(head):
"""
:type head: ListNode
:rtype: ListNode
"""
if not head or not head.next:
return head
prev = None
while head:
current = head
head = head.next
current.next = prev
prev = current
return prev |
def reverse_list_recursive(head):
"""
:type head: ListNode
:rtype: ListNode
"""
if head is None or head.next is None:
return head
p = head.next
head.next = None
revrest = reverse_list_recursive(p)
p.next = head
return revrest |
def has_path_sum(root, sum):
"""
:type root: TreeNode
:type sum: int
:rtype: bool
"""
if root is None:
return False
if root.left is None and root.right is None and root.val == sum:
return True
sum -= root.val
return has_path_sum(root.left, sum) or has_path_sum(root.ri... |
def int_to_base(n, base):
"""
:type n: int
:type base: int
:rtype: str
"""
is_negative = False
if n == 0:
return '0'
elif n < 0:
is_negative = True
n *= -1
digit = string.digits + string.ascii_uppercase
res = ''
while n > 0:
res += ... |
def base_to_int(s, base):
"""
Note : You can use int() built-in function instread of this.
:type s: str
:type base: int
:rtype: int
"""
digit = {}
for i,c in enumerate(string.digits + string.ascii_uppercase):
digit[c] = i
multiplier = 1
res = 0
fo... |
def is_cyclic(head):
"""
:type head: Node
:rtype: bool
"""
if not head:
return False
runner = head
walker = head
while runner.next and runner.next.next:
runner = runner.next.next
walker = walker.next
if runner == walker:
return True
return ... |
def decode_string(s):
"""
:type s: str
:rtype: str
"""
stack = []; cur_num = 0; cur_string = ''
for c in s:
if c == '[':
stack.append((cur_string, cur_num))
cur_string = ''
cur_num = 0
elif c == ']':
prev_string, num = stack.pop()
... |
def palindromic_substrings_iter(s):
"""
A slightly more Pythonic approach with a recursive generator
"""
if not s:
yield []
return
for i in range(len(s), 0, -1):
sub = s[:i]
if sub == sub[::-1]:
for rest in palindromic_substrings_iter(s[i:]):
... |
def is_isomorphic(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
if len(s) != len(t):
return False
dict = {}
set_value = set()
for i in range(len(s)):
if s[i] not in dict:
if t[i] in set_value:
return False
dict[s[i]] = t... |
def calc(n2, n1, operator):
"""
Calculate operation result
n2 Number: Number 2
n1 Number: Number 1
operator Char: Operation to calculate
"""
if operator == '-': return n1 - n2
elif operator == '+': return n1 + n2
elif operator == '*': return n1 * n2
elif operator == '... |
def apply_operation(op_stack, out_stack):
"""
Apply operation to the first 2 items of the output queue
op_stack Deque (reference)
out_stack Deque (reference)
"""
out_stack.append(calc(out_stack.pop(), out_stack.pop(), op_stack.pop())) |
def parse(expression):
"""
Return array of parsed tokens in the expression
expression String: Math expression to parse in infix notation
"""
result = []
current = ""
for i in expression:
if i.isdigit() or i == '.':
current += i
else:
if le... |
def evaluate(expression):
"""
Calculate result of expression
expression String: The expression
type Type (optional): Number type [int, float]
"""
op_stack = deque() # operator stack
out_stack = deque() # output stack (values)
tokens = parse(expression) # calls the function onl... |
def main():
"""
simple user-interface
"""
print("\t\tCalculator\n\n")
while True:
user_input = input("expression or exit: ")
if user_input == "exit":
break
try:
print("The result is {0}".format(evaluate(user_input)))
except Excep... |
def closest_value(root, target):
"""
:type root: TreeNode
:type target: float
:rtype: int
"""
a = root.val
kid = root.left if target < a else root.right
if not kid:
return a
b = closest_value(kid, target)
return min((a,b), key=lambda x: abs(target-x)) |
def get_primes(n):
"""Return list of all primes less than n,
Using sieve of Eratosthenes.
"""
if n <= 0:
raise ValueError("'n' must be a positive integer.")
# If x is even, exclude x from list (-1):
sieve_size = (n // 2 - 1) if n % 2 == 0 else (n // 2)
sieve = [True for _ in range(si... |
def permute(elements):
"""
returns a list with the permuations.
"""
if len(elements) <= 1:
return [elements]
else:
tmp = []
for perm in permute(elements[1:]):
for i in range(len(elements)):
tmp.append(perm[:i] + elements[0:1] + perm[i:])
... |
def permute_iter(elements):
"""
iterator: returns a perumation by each call.
"""
if len(elements) <= 1:
yield elements
else:
for perm in permute_iter(elements[1:]):
for i in range(len(elements)):
yield perm[:i] + elements[0:1] + perm[i:] |
def extended_gcd(a, b):
"""Extended GCD algorithm.
Return s, t, g
such that a * s + b * t = GCD(a, b)
and s and t are co-prime.
"""
old_s, s = 1, 0
old_t, t = 0, 1
old_r, r = a, b
while r != 0:
quotient = old_r / r
old_r, r = r, old_r - quotient * r
... |
def bin_tree_to_list(root):
"""
type root: root class
"""
if not root:
return root
root = bin_tree_to_list_util(root)
while root.left:
root = root.left
return root |
def add_operators(num, target):
"""
:type num: str
:type target: int
:rtype: List[str]
"""
def dfs(res, path, num, target, pos, prev, multed):
if pos == len(num):
if target == prev:
res.append(path)
return
for i in range(pos, len(num)):
... |
def _init_rabit():
"""internal library initializer."""
if _LIB is not None:
_LIB.RabitGetRank.restype = ctypes.c_int
_LIB.RabitGetWorldSize.restype = ctypes.c_int
_LIB.RabitIsDistributed.restype = ctypes.c_int
_LIB.RabitVersionNumber.restype = ctypes.c_int |
def init(args=None):
"""Initialize the rabit library with arguments"""
if args is None:
args = []
arr = (ctypes.c_char_p * len(args))()
arr[:] = args
_LIB.RabitInit(len(arr), arr) |
def tracker_print(msg):
"""Print message to the tracker.
This function can be used to communicate the information of
the progress to the tracker
Parameters
----------
msg : str
The message to be printed to tracker.
"""
if not isinstance(msg, STRING_TYPES):
msg = str(msg... |
def get_processor_name():
"""Get the processor name.
Returns
-------
name : str
the name of processor(host)
"""
mxlen = 256
length = ctypes.c_ulong()
buf = ctypes.create_string_buffer(mxlen)
_LIB.RabitGetProcessorName(buf, ctypes.byref(length), mxlen)
return buf.value |
def broadcast(data, root):
"""Broadcast object from one node to all other nodes.
Parameters
----------
data : any type that can be pickled
Input data, if current rank does not equal root, this can be None
root : int
Rank of the node to broadcast data from.
Returns
-------
... |
def normpath(path):
"""Normalize UNIX path to a native path."""
normalized = os.path.join(*path.split("/"))
if os.path.isabs(path):
return os.path.abspath("/") + normalized
else:
return normalized |
def _train_internal(params, dtrain,
num_boost_round=10, evals=(),
obj=None, feval=None,
xgb_model=None, callbacks=None):
"""internal training function"""
callbacks = [] if callbacks is None else callbacks
evals = list(evals)
if isinstance(param... |
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
maximize=False, early_stopping_rounds=None, evals_result=None,
verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside... |
def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
folds=None, shuffle=True):
"""
Make an n-fold list of CVPack from random indices.
"""
evals = list(evals)
np.random.seed(seed)
if stratified is False and folds is None:
# Do standard k-fold cros... |
def aggcv(rlist):
# pylint: disable=invalid-name
"""
Aggregate cross-validation results.
If verbose_eval is true, progress is displayed in every call. If
verbose_eval is an integer, progress will only be displayed every
`verbose_eval` trees, tracked via trial.
"""
cvmap = {}
idx = r... |
def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None,
metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None,
fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True,
seed=0, callbacks=None, shuffle=True):
# pylint: disable = invalid-na... |
def update(self, iteration, fobj):
""""Update the boosters for one iteration"""
self.bst.update(self.dtrain, iteration, fobj) |
def eval(self, iteration, feval):
""""Evaluate the CVPack for one iteration."""
return self.bst.eval_set(self.watchlist, iteration, feval) |
def _get_callback_context(env):
"""return whether the current callback context is cv or train"""
if env.model is not None and env.cvfolds is None:
context = 'train'
elif env.model is None and env.cvfolds is not None:
context = 'cv'
return context |
def _fmt_metric(value, show_stdv=True):
"""format metric string"""
if len(value) == 2:
return '%s:%g' % (value[0], value[1])
if len(value) == 3:
if show_stdv:
return '%s:%g+%g' % (value[0], value[1], value[2])
return '%s:%g' % (value[0], value[1])
raise ValueError("wr... |
def print_evaluation(period=1, show_stdv=True):
"""Create a callback that print evaluation result.
We print the evaluation results every **period** iterations
and on the first and the last iterations.
Parameters
----------
period : int
The period to log the evaluation results
show... |
def record_evaluation(eval_result):
"""Create a call back that records the evaluation history into **eval_result**.
Parameters
----------
eval_result : dict
A dictionary to store the evaluation results.
Returns
-------
callback : function
The requested callback function.
... |
def reset_learning_rate(learning_rates):
"""Reset learning rate after iteration 1
NOTE: the initial learning rate will still take in-effect on first iteration.
Parameters
----------
learning_rates: list or function
List of learning rate for each boosting round
or a customized funct... |
def early_stop(stopping_rounds, maximize=False, verbose=True):
"""Create a callback that activates early stoppping.
Validation error needs to decrease at least
every **stopping_rounds** round(s) to continue training.
Requires at least one item in **evals**.
If there's more than one, will use the la... |
def run_doxygen(folder):
"""Run the doxygen make command in the designated folder."""
try:
retcode = subprocess.call("cd %s; make doxygen" % folder, shell=True)
if retcode < 0:
sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
except OSError as e:
sys.stderr.write("doxygen executi... |
def _objective_decorator(func):
"""Decorate an objective function
Converts an objective function using the typical sklearn metrics
signature so that it is usable with ``xgboost.training.train``
Parameters
----------
func: callable
Expects a callable with signature ``func(y_true, y_pred... |
def set_params(self, **params):
"""Set the parameters of this estimator.
Modification of the sklearn method to allow unknown kwargs. This allows using
the full range of xgboost parameters that are not defined as member variables
in sklearn grid search.
Returns
-------
... |
def get_params(self, deep=False):
"""Get parameters."""
params = super(XGBModel, self).get_params(deep=deep)
if isinstance(self.kwargs, dict): # if kwargs is a dict, update params accordingly
params.update(self.kwargs)
if params['missing'] is np.nan:
params['miss... |
def get_xgb_params(self):
"""Get xgboost type parameters."""
xgb_params = self.get_params()
random_state = xgb_params.pop('random_state')
if 'seed' in xgb_params and xgb_params['seed'] is not None:
warnings.warn('The seed parameter is deprecated as of version .6.'
... |
def load_model(self, fname):
"""
Load the model from a file.
The model is loaded from an XGBoost internal binary format which is
universal among the various XGBoost interfaces. Auxiliary attributes of
the Python Booster object (such as feature names) will not be loaded.
... |
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
Fit the gra... |
def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
"""
Predict with `data`.
.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thr... |
def apply(self, X, ntree_limit=0):
"""Return the predicted leaf every tree for each sample.
Parameters
----------
X : array_like, shape=[n_samples, n_features]
Input features matrix.
ntree_limit : int
Limit number of trees in the prediction; defaults to ... |
def feature_importances_(self):
"""
Feature importances property
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base
learner (`booster=gbtree`). It is not defined for other base... |
def coef_(self):
"""
Coefficients property
.. note:: Coefficients are defined only for linear learners
Coefficients are only defined when the linear model is chosen as base
learner (`booster=gblinear`). It is not defined for other base learner types, such
as... |
def intercept_(self):
"""
Intercept (bias) property
.. note:: Intercept is defined only for linear learners
Intercept (bias) is only defined when the linear model is chosen as base
learner (`booster=gblinear`). It is not defined for other base learner types, such
... |
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
# pylint: disable = attribute-defined-outside-init,arguments-differ
"""
Fit gradient boosting c... |
def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
"""
Predict with `data`.
.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thr... |
def predict_proba(self, data, ntree_limit=None, validate_features=True):
"""
Predict the probability of each `data` example being of a given class.
.. note:: This function is not thread safe
For each booster object, predict can only be called from one thread.
If you wan... |
def fit(self, X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None,
eval_group=None, eval_metric=None, early_stopping_rounds=None,
verbose=False, xgb_model=None, callbacks=None):
# pylint: disable = attribute-defined-outside-init,arguments-differ
"""
... |
def from_pystr_to_cstr(data):
"""Convert a list of Python str to C pointer
Parameters
----------
data : list
list of str
"""
if not isinstance(data, list):
raise NotImplementedError
pointers = (ctypes.c_char_p * len(data))()
if PY3:
data = [bytes(d, 'utf-8') for... |
def from_cstr_to_pystr(data, length):
"""Revert C pointer to Python str
Parameters
----------
data : ctypes pointer
pointer to data
length : ctypes pointer
pointer to length of data
"""
if PY3:
res = []
for i in range(length.value):
try:
... |
def _load_lib():
"""Load xgboost Library."""
lib_paths = find_lib_path()
if not lib_paths:
return None
try:
pathBackup = os.environ['PATH'].split(os.pathsep)
except KeyError:
pathBackup = []
lib_success = False
os_error_list = []
for lib_path in lib_paths:
... |
def ctypes2numpy(cptr, length, dtype):
"""Convert a ctypes pointer array to a numpy array.
"""
NUMPY_TO_CTYPES_MAPPING = {
np.float32: ctypes.c_float,
np.uint32: ctypes.c_uint,
}
if dtype not in NUMPY_TO_CTYPES_MAPPING:
raise RuntimeError('Supported types: {}'.format(NUMPY_TO... |
def ctypes2buffer(cptr, length):
"""Convert ctypes pointer to buffer type."""
if not isinstance(cptr, ctypes.POINTER(ctypes.c_char)):
raise RuntimeError('expected char pointer')
res = bytearray(length)
rptr = (ctypes.c_char * length).from_buffer(res)
if not ctypes.memmove(rptr, cptr, length)... |
def c_array(ctype, values):
"""Convert a python string to c array."""
if isinstance(values, np.ndarray) and values.dtype.itemsize == ctypes.sizeof(ctype):
return (ctype * len(values)).from_buffer_copy(values)
return (ctype * len(values))(*values) |
def _maybe_pandas_data(data, feature_names, feature_types):
""" Extract internal data from pd.DataFrame for DMatrix data """
if not isinstance(data, DataFrame):
return data, feature_names, feature_types
data_dtypes = data.dtypes
if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in data_dt... |
def _maybe_dt_data(data, feature_names, feature_types):
"""
Validate feature names and types if data table
"""
if not isinstance(data, DataTable):
return data, feature_names, feature_types
data_types_names = tuple(lt.name for lt in data.ltypes)
bad_fields = [data.names[i]
... |
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