Added W02 materials

Author: IvanSol

README.md

@@ -40,8 +40,8 @@
 | Week   | Content                | Slides               | WarmUp test             | Contest                  | Soft Deadline          |
 |:------:|:-----------------------|:--------------------:|:-----------------------:|:------------------------:|:----------------------:|
 | 01     | Sorting algorithms     | [Slides][Slides_W01] | [Test][WarmUp_test_W01] | [Contest][ContestID_W01] | 07.05.2024 09:00 UTC+7 |
-<!---
 | 02     | Binary search          | [Slides][Slides_W02] | [Test][WarmUp_test_W02] | [Contest][ContestID_W02] | 08.05.2024 09:00 UTC+7 |
+<!---
 | 03     | Basic Data sturctures  | [Slides][Slides_W03] | [Test][WarmUp_test_W03] | [Contest][ContestID_W03] | 09.05.2024 09:00 UTC+7 |
 | 04     | Dynamic programming    | [Slides][Slides_W04] | [Test][WarmUp_test_W04] | [Contest][ContestID_W04] | 10.05.2024 09:00 UTC+7 |
 | 05     | Knapsack problem       | [Slides][Slides_W05] | [Test][WarmUp_test_W05] | [Contest][ContestID_W05] | 13.05.2024 09:00 UTC+7 |

week02_binary_search/HS.BKK.2024.Algo.Class02.pdf

@@ -0,0 +1,53 @@
+# This is a template for problem Sigma-Trimpazation.
+
+from time import time
+
+# set the following flag to True to estimate execution time
+#
+# Exact execution time on you device may be different
+# from one on Yandex contest platform!
+#
+# The flag MUST be False when you are submitting your solution!
+estimate_execution_time = False
+
+# Fixed parameters of quantum process:
+quantum_a = 7**5
+quantum_m = 2**31 - 1
+
+
+def analyze_trimpazation(n, m, q0):
+    '''
+    This function generates data with given parameters
+    and calculates desired Y value.
+
+    You need to modify it to make it execute faster.
+    You can check your progress using estimate_execution_time flag
+    at the top of the file.
+    '''
+    m_div2 = m // 2
+    q = q0
+
+    # generating x data:
+    x = []
+    for i in range(n):
+        x_i = q % m - m_div2
+        x.append(x_i)
+        q = ((q * quantum_a) % quantum_m)
+
+    # sorting x data:
+    x.sort()
+
+    # calculating sum:
+    res = 0
+    for i, v in enumerate(x):
+        res += (i + 1) * v
+
+    return res
+
+
+if __name__ == '__main__':
+    N, M, q0 = map(int, input().split())
+    t = time()
+    print(analyze_trimpazation(N, M, q0))
+    if estimate_execution_time:
+        print(f'Execution time = {time() - t:.8f} seconds')

week02_binary_search/W2.B.Trimpazation_template.py

@@ -0,0 +1,41 @@
+def binsearch_exists(x, key):
+    l = 0
+    r = len(x)
+    while r - l > 1:
+        m = (l + r) // 2
+        if x[m] <= key:
+            l = m
+        else:
+            r = m
+    return x[l] == key
+
+
+def binsearch_left(x, key):
+    l = -1
+    r = len(x)
+    while r - l > 1:
+        m = (l + r) // 2
+        if x[m] < key:
+            l = m
+        else:
+            r = m
+    return r
+
+
+def binsearch_right(x, key):
+    l = -1
+    r = len(x)
+    while r - l > 1:
+        m = (l + r) // 2
+        if x[m] <= key:
+            l = m
+        else:
+            r = m
+    return r
+
+
+N = int(input())
+x = list(map(int, input().split()))
+x = sorted(x)
+key = int(input())
+print("YES" if binsearch_exists(x, key) else "NO")

week02_binary_search/binserarch_exists.py

@@ -0,0 +1,35 @@
+def counting_sort(x):
+    N = len(x)
+    M = max(x) + 1
+    a = [0] * M
+    for v in x:
+        a[v] += 1
+    res = []
+    for i in range(M):
+        for j in range(a[i]):
+            res.append(i)
+    return res
+
+
+def counting_sort2(x):
+    N = len(x)
+    M = max(x) + 1
+    c = [0] * M
+    for v in x:
+        c[v] += 1
+    for i in range(1, M):
+        c[i] += c[i - 1]
+    # print(c)
+    res = [None] * N
+    for i in range(N):
+        position = c[x[i]] - 1
+        res[position] = x[i]
+        c[x[i]] -= 1
+    return res
+
+
+if __name__ == '__main__':
+    N = int(input())
+    x = list(map(int, input().split()))
+    x = counting_sort2(x)
+    print(' '.join(map(str, x)))

week02_binary_search/counting.py

@@ -0,0 +1,25 @@
+from math import sqrt as msqrt
+from random import random
+
+def sqrt(x, eps):
+    l = 0.
+    r = max(1, float(x))
+    while r - l > eps:
+        m = (l + r) / 2
+        if m ** 2 < x:
+            l = m
+        else:
+            r = m
+    return (l + r) / 2
+
+
+eps = 1e-8
+while True:
+    x = random() * 100
+    y1 = msqrt(x)
+    y2 = sqrt(x, eps)
+    if abs(y1 - y2) > eps:
+        print(f'fail: {x:.9f}')
+        exit(0)
+    else:
+        print('OK')