"Б12-513 Ибрагимов Роберт"

homeworks/sem01/hw1/backoff.py

@@ -1,5 +1,3 @@
-from random import uniform
-from time import sleep
 from typing import (
     Callable,
     ParamSpec,

solutions/sem01/lesson02/task3.py

@@ -1,4 +1,4 @@
 def get_amount_of_ways_to_climb(stair_amount: int) -> int:
-    step_prev, step_curr = 1, 1
+    _step_prev, step_curr = 1, 1
     # ваш код
     return step_curr

solutions/sem01/lesson03/task1.py

@@ -1,3 +1,3 @@
 def flip_bits_in_range(num: int, left_bit: int, right_bit: int) -> int:
     # ваш код
-    return num
+    return num

solutions/sem01/lesson03/task2.py

@@ -1,3 +1,3 @@
 def get_cube_root(n: float, eps: float) -> float:
     # ваш код
-    return n
+    return n

solutions/sem01/lesson04/task1.py

@@ -1,3 +1,3 @@
 def is_arithmetic_progression(lst: list[list[int]]) -> bool:
     # ваш код
-    return False
+    return False

solutions/sem01/lesson04/task2.py

@@ -1,3 +1,3 @@
 def merge_intervals(intervals: list[list[int, int]]) -> list[list[int, int]]:
     # ваш код
-    return [[0,0]]
+    return [[0, 0]]

solutions/sem01/lesson04/task4.py

@@ -1,3 +1,3 @@
 def move_zeros_to_end(nums: list[int]) -> list[int]:
     # ваш код
-    return 0
+    return 0

solutions/sem01/lesson04/task5.py

@@ -1,3 +1,3 @@
 def find_row_with_most_ones(matrix: list[list[int]]) -> int:
     # ваш код
-    return 0
+    return 0

solutions/sem01/lesson04/task6.py

@@ -1,3 +1,3 @@
-def count_cycles(arr: list[int]) -> int: 
+def count_cycles(arr: list[int]) -> int:
     # ваш код
-    return 0
+    return 0

solutions/sem01/lesson05/task1.py

@@ -1,3 +1,3 @@
 def is_palindrome(text: str) -> bool:
     # ваш код
-    return False
+    return False

solutions/sem01/lesson05/task2.py

@@ -1,3 +1,3 @@
 def are_anagrams(word1: str, word2: str) -> bool:
     # ваш код
-    return False
+    return False

solutions/sem01/lesson05/task4.py

@@ -1,3 +1,3 @@
 def unzip(compress_text: str) -> str:
     # ваш код
-    return compress_text
+    return compress_text

solutions/sem01/lesson05/task5.py

@@ -1,3 +1,3 @@
-def reg_validator(reg_expr: str, text: str) -> bool:  
+def reg_validator(reg_expr: str, text: str) -> bool:
     # ваш код
-    return False
+    return False

solutions/sem01/lesson05/task6.py

@@ -1,3 +1,3 @@
 def simplify_path(path: str) -> str:
     # ваш код
-    return path
+    return path

solutions/sem01/lesson06/task1.py

@@ -1,3 +1,3 @@
 def int_to_roman(num: int) -> str:
     # ваш код
-    return ""
+    return ""

solutions/sem01/lesson06/task2.py

@@ -1,3 +1,3 @@
 def get_len_of_longest_substring(text: str) -> int:
     # ваш код
-    return 0
+    return 0

solutions/sem01/lesson06/task3.py

@@ -2,6 +2,5 @@ def is_there_any_good_subarray(
     nums: list[int],
     k: int,
 ) -> bool:
-
     # ваш код
     return False

solutions/sem01/lesson06/task4.py

@@ -1,3 +1,3 @@
 def count_unique_words(text: str) -> int:
     # ваш код
-    return 0
+    return 0

solutions/sem01/lesson08/task1.py

@@ -1,5 +1,6 @@
 from typing import Callable
 
+
 def make_averager(accumulation_period: int) -> Callable[[float], float]:
     # ваш код
-    pass
+    pass

solutions/sem01/lesson08/task2.py

@@ -2,9 +2,7 @@
 
 T = TypeVar("T")
 
-def collect_statistic(
-    statistics: dict[str, list[float, int]]
-) -> Callable[[T], T]:
-
+
+def collect_statistic(statistics: dict[str, list[float, int]]) -> Callable[[T], T]:
     # ваш код
-    pass
+    pass

solutions/sem01/lesson12/task3.py

@@ -1,6 +1,3 @@
-import sys
-
-
 class FileOut:
     def __init__(
         self,

solutions/sem02/lesson03/task1.py

@@ -8,13 +8,23 @@ class ShapeMismatchError(Exception):
 def sum_arrays_vectorized(
     lhs: np.ndarray,
     rhs: np.ndarray,
-) -> np.ndarray: ...
+) -> np.ndarray:
+    if lhs.shape != rhs.shape:
+        raise ShapeMismatchError
+    return np.add(lhs, rhs)
 
 
-def compute_poly_vectorized(abscissa: np.ndarray) -> np.ndarray: ...
+def compute_poly_vectorized(abscissa: np.ndarray) -> np.ndarray:
+    return 3 * (abscissa**2) + 2 * abscissa + 1
 
 
 def get_mutual_l2_distances_vectorized(
     lhs: np.ndarray,
     rhs: np.ndarray,
-) -> np.ndarray: ...
+) -> np.ndarray:
+    if lhs.shape[1] != rhs.shape[1]:
+        raise ShapeMismatchError
+    new_lhs = lhs[:, np.newaxis, :]
+    new_rhs = rhs[np.newaxis, :]
+    difference = new_lhs - new_rhs
+    return np.sqrt(np.sum(difference**2, axis=2))

solutions/sem02/lesson03/task2.py

@@ -1,4 +1,5 @@
 import numpy as np
+from numpy.ma.core import arccos, arctan2
 
 
 class ShapeMismatchError(Exception):
@@ -9,11 +10,31 @@ def convert_from_sphere(
     distances: np.ndarray,
     azimuth: np.ndarray,
     inclination: np.ndarray,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ...
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    if (
+        (distances.shape != azimuth.shape)
+        or (distances.shape != inclination.shape)
+        or (azimuth.shape != inclination.shape)
+    ):
+        raise ShapeMismatchError
+    abscissa = distances * np.sin(inclination) * np.cos(azimuth)
+    ordinates = distances * np.sin(inclination) * np.sin(azimuth)
+    applicates = distances * np.cos(inclination)
+    return abscissa, ordinates, applicates
 
 
 def convert_to_sphere(
     abscissa: np.ndarray,
     ordinates: np.ndarray,
     applicates: np.ndarray,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ...
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    if (
+        (abscissa.shape != ordinates.shape)
+        or (abscissa.shape != applicates.shape)
+        or (ordinates.shape != applicates.shape)
+    ):
+        raise ShapeMismatchError
+    distances = (abscissa**2 + ordinates**2 + applicates**2) ** 0.5
+    inclication = arccos(applicates / distances)
+    azimuth = arctan2(ordinates, abscissa)
+    return distances, azimuth, inclication

solutions/sem02/lesson03/task3.py

@@ -3,4 +3,12 @@
 
 def get_extremum_indices(
     ordinates: np.ndarray,
-) -> tuple[np.ndarray, np.ndarray]: ...
+) -> tuple[np.ndarray, np.ndarray]:
+    if len(ordinates) < 3:
+        raise ValueError
+    mask_for_maximums = (ordinates[1:-1] > ordinates[:-2]) & (ordinates[1:-1] > ordinates[2:])
+    mask_for_minimums = (ordinates[1:-1] < ordinates[:-2]) & (ordinates[1:-1] < ordinates[2:])
+
+    index_of_minimum = np.arange(1, len(ordinates) - 1)[mask_for_minimums]
+    index_of_maximum = np.arange(1, len(ordinates) - 1)[mask_for_maximums]
+    return index_of_minimum, index_of_maximum