Б12-513, Рамазанов Булат

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_curr = 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

@@ -1,7 +0,0 @@
-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

@@ -1,10 +0,0 @@
-from typing import Callable, TypeVar
-
-T = TypeVar("T")
-
-def collect_statistic(
-    statistics: dict[str, list[float, int]]
-) -> Callable[[T], T]:
-
-    # ваш код
-    pass

solutions/sem01/lesson12/task3.py

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

solutions/sem02/lesson03/task1.py

@@ -8,13 +8,22 @@ 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 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
+    diff = lhs[:, np.newaxis, :] - rhs[np.newaxis, :, :]
+    distances = np.linalg.norm(diff, axis=-1)
+    return distances

solutions/sem02/lesson03/task2.py

@@ -9,11 +9,25 @@ 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:
+        raise ShapeMismatchError
+    x = distances * np.cos(azimuth) * np.sin(inclination)
+    y = distances * np.sin(azimuth) * np.sin(inclination)
+    z = distances * np.cos(inclination)
+    return x, y, z
 
 
 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:
+        raise ShapeMismatchError
+    r = np.sqrt(abscissa**2 + ordinates**2 + applicates**2)
+    azimuth = np.arctan2(ordinates, abscissa)
+    inclination = np.zeros_like(r)
+    mask = r != 0
+    inclination[mask] = np.arccos(applicates[mask] / r[mask])
+    return r, azimuth, inclination

solutions/sem02/lesson03/task3.py

@@ -3,4 +3,14 @@
 
 def get_extremum_indices(
     ordinates: np.ndarray,
-) -> tuple[np.ndarray, np.ndarray]: ...
+) -> tuple[np.ndarray, np.ndarray]:
+    if ordinates.shape < (3,):
+        raise ValueError
+    indexs = np.arange(1, len(ordinates) - 1)
+    sp_min = (ordinates[indexs] < ordinates[indexs - 1]) & (
+        ordinates[indexs] < ordinates[indexs + 1]
+    )
+    sp_max = (ordinates[indexs] > ordinates[indexs - 1]) & (
+        ordinates[indexs] > ordinates[indexs + 1]
+    )
+    return indexs[sp_min], indexs[sp_max]

solutions/sem02/lesson04/task1.py

@@ -2,16 +2,42 @@
 
 
 def pad_image(image: np.ndarray, pad_size: int) -> np.ndarray:
-    # ваш код
-    return image
+    if pad_size < 1 or image.ndim > 3:
+        raise ValueError
+    if image.ndim == 2:
+        height, weight = image.shape
+        res_image = np.zeros((height + 2 * pad_size, weight + 2 * pad_size), dtype=image.dtype)
+        res_image[pad_size : pad_size + height, pad_size : pad_size + weight] = image
+    if image.ndim == 3:
+        height, weight, razm = image.shape
+        res_image = np.zeros(
+            (height + 2 * pad_size, weight + 2 * pad_size, razm), dtype=image.dtype
+        )
+        res_image[pad_size : pad_size + height, pad_size : pad_size + weight, :] = image
+    return res_image
 
 
 def blur_image(
     image: np.ndarray,
     kernel_size: int,
 ) -> np.ndarray:
-    # ваш код
-    return image
+    if kernel_size < 1 or kernel_size % 2 == 0:
+        raise ValueError
+    elif kernel_size == 1:
+        return image
+    res_image = np.zeros(image.shape, dtype=np.float64)
+    padded_image = pad_image(image, kernel_size // 2)
+    if image.ndim == 2:
+        for i in range(image.shape[0]):
+            for j in range(image.shape[1]):
+                res_image[i, j] = np.mean(padded_image[i : i + kernel_size, j : j + kernel_size])
+    if image.ndim == 3:
+        for i in range(image.shape[0]):
+            for j in range(image.shape[1]):
+                res_image[i, j] = np.mean(
+                    padded_image[i : i + kernel_size, j : j + kernel_size, :], axis=(0, 1)
+                )
+    return np.round(res_image).astype(image.dtype)
 
 
 if __name__ == "__main__":

solutions/sem02/lesson04/task2.py

@@ -5,6 +5,19 @@ def get_dominant_color_info(
     image: np.ndarray[np.uint8],
     threshold: int = 5,
 ) -> tuple[np.uint8, float]:
-    # ваш код
-
-    return 0, 0
+    if threshold < 1:
+        raise ValueError("threshold must be positive")
+    pixels = np.sort(image.flatten())
+    max_color = 0
+    max_count = 0
+    left = 0
+    center = 0
+    for right in range(len(pixels)):
+        while pixels[right] - pixels[center] >= threshold:
+            center += 1
+        while pixels[center] - pixels[left] >= threshold:
+            left += 1
+        if right - left + 1 > max_count:
+            max_count = right - left + 1
+            max_color = pixels[center]
+    return max_color, max_count * 100 / len(pixels)

solutions/sem02/lesson05/task1.py

@@ -9,4 +9,15 @@ def can_satisfy_demand(
     costs: np.ndarray,
     resource_amounts: np.ndarray,
     demand_expected: np.ndarray,
-) -> bool: ...
+) -> bool:
+    if np.ndim(costs) != 2:
+        raise ShapeMismatchError
+    M, N = costs.shape
+    if np.ndim(resource_amounts) != 1 or resource_amounts.size != M:
+        raise ShapeMismatchError
+
+    if np.ndim(demand_expected) != 1 or demand_expected.size != N:
+        raise ShapeMismatchError
+
+    resources_needed = np.sum(costs * demand_expected, axis=1)
+    return all(resources_needed <= resource_amounts)

solutions/sem02/lesson05/task2.py

@@ -8,4 +8,13 @@ class ShapeMismatchError(Exception):
 def get_projections_components(
     matrix: np.ndarray,
     vector: np.ndarray,
-) -> tuple[np.ndarray | None, np.ndarray | None]: ...
+) -> tuple[np.ndarray | None, np.ndarray | None]:
+    if matrix.shape[0] != matrix.shape[1] or matrix.shape[1] != vector.shape[0]:
+        raise ShapeMismatchError
+    if not np.linalg.det(matrix):
+        return None, None
+    scalar = matrix @ vector
+    vector_square = np.sum(matrix**2, axis=1)
+    projections = (matrix.T * scalar / vector_square).T
+    components = vector - projections
+    return projections, components

solutions/sem02/lesson05/task3.py

@@ -9,4 +9,14 @@ def adaptive_filter(
     Vs: np.ndarray,
     Vj: np.ndarray,
     diag_A: np.ndarray,
-) -> np.ndarray: ...
+) -> np.ndarray:
+    if np.ndim(Vs) != 2 or np.ndim(Vj) != 2 or np.ndim(diag_A) != 1:
+        raise ShapeMismatchError
+
+    if Vj.shape[0] != Vs.shape[0] or Vj.shape[1] != diag_A.size:
+        raise ShapeMismatchError
+
+    A = np.diag(diag_A)
+    Vj_H = (Vj.real - 1j * Vj.imag).T
+    R_1 = np.linalg.inv((np.eye(diag_A.size) + Vj_H @ Vj @ A))
+    return Vs - Vj @ R_1 @ (Vj_H @ Vs)

solutions/sem02/lesson07/task1.py

@@ -13,8 +13,59 @@ def visualize_diagrams(
     ordinates: np.ndarray,
     diagram_type: Any,
 ) -> None:
-    # ваш код
-    pass
+    if abscissa.shape != ordinates.shape:
+        raise ShapeMismatchError
+
+    valid_types = {"hist", "violin", "box"}
+    if diagram_type not in valid_types:
+        raise ValueError
+
+    fig = plt.figure(figsize=(8, 8))
+    grid = plt.GridSpec(4, 4, wspace=0.2, hspace=0.2)
+
+    ax_scat = fig.add_subplot(grid[:-1, 1:])
+    ax_v = fig.add_subplot(grid[:-1, 0], sharey=ax_scat)
+    ax_h = fig.add_subplot(grid[-1, 1:], sharex=ax_scat)
+
+    ax_scat.scatter(abscissa, ordinates, color="steelblue", alpha=0.35)
+    ax_scat.set_title("Distribution", fontsize=17, fontweight="bold", c="black")
+
+    if diagram_type == "hist":
+        hist_params = {
+            "bins": 50,
+            "color": "steelblue",
+            "edgecolor": "navy",
+            "alpha": 0.7,
+            "density": True,
+        }
+        ax_h.hist(abscissa, **hist_params)
+        ax_v.hist(ordinates, orientation="horizontal", **hist_params)
+        ax_h.invert_yaxis()
+        ax_v.invert_xaxis()
+
+    elif diagram_type == "violin":
+
+        def set_violin(axis, data, vertical):
+            parts = axis.violinplot(data, vert=vertical, showmedians=True)
+            for body in parts["bodies"]:
+                body.set_facecolor("steelblue")
+                body.set_edgecolor("lightsteelblue")
+            for part in parts:
+                if part == "bodies":
+                    continue
+                parts[part].set_edgecolor("steelblue")
+
+        set_violin(ax_h, ordinates, vertical=False)
+        set_violin(ax_v, abscissa, vertical=True)
+
+    elif diagram_type == "box":
+        box_props = {"facecolor": "steelblue", "alpha": 0.7}
+        box_params = {"patch_artist": True, "boxprops": box_props}
+        ax_v.boxplot(abscissa, vert=True, **box_params)
+        ax_h.boxplot(ordinates, vert=False, **box_params)
+
+    ax_v.set_ylabel("Vertical", fontsize=17, fontweight="bold", c="dimgray")
+    ax_h.set_xlabel("Horizontal", fontsize=17, fontweight="bold", c="dimgray")
 
 
 if __name__ == "__main__":