108 lines
3.2 KiB
Plaintext
108 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f75546c6-5eb9-4b1e-be1a-ec2bf162b848",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--- 1. Двумерный массив ---\n",
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"[[1 2]\n",
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" [3 4]]\n",
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"\n",
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"--- 2. Равномерные интервалы (linspace) ---\n",
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"[0. 0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
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" 3.33333333 3.88888889 4.44444444 5. ]\n",
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"\n",
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"--- 3. Случайные числа (randn) ---\n",
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"[[-0.54989953 -0.65009822]\n",
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" [-0.65433968 0.85084421]]\n",
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"\n",
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"--- 4. Результат np.dot() ---\n",
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"[[-1.8585789 1.0515902 ]\n",
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" [-4.26705733 1.45308217]]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[-1.8585789 , 1.0515902 ],\n",
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" [-4.26705733, 1.45308217]])"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"# 1. Создание двумерного массива (матрицы 2x2)\n",
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"matrix_2x2 = np.array([[1, 2], [3, 4]])\n",
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"\n",
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"# 2. Использование np.linspace() \n",
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"# Создаем 10 равномерно распределенных чисел от 0 до 5\n",
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"lin_points = np.linspace(0, 5, 10)\n",
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"\n",
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"# 3. Использование np.random.randn()\n",
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"# Генерируем случайную матрицу 2x2 из нормального распределения\n",
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"random_matrix = np.random.randn(2, 2)\n",
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"\n",
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"# 4. Использование np.dot()\n",
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"# Выполняем умножение двух матриц (нашей первой матрицы и случайной)\n",
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"matrix_product = np.dot(matrix_2x2, random_matrix)\n",
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"\n",
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"# Вывод всех результатов\n",
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"print(\"--- 1. Двумерный массив ---\")\n",
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"print(matrix_2x2)\n",
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"\n",
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"print(\"\\n--- 2. Равномерные интервалы (linspace) ---\")\n",
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"print(lin_points)\n",
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"\n",
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"print(\"\\n--- 3. Случайные числа (randn) ---\")\n",
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"print(random_matrix)\n",
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"\n",
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"print(\"\\n--- 4. Результат np.dot() ---\")\n",
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"print(matrix_product)\n",
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"\n",
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"# Последняя строчка для отображения в интерактивной среде\n",
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"matrix_product"
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]
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},
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{
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"execution_count": null,
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"id": "e7ad4d46-abc8-4dea-b290-1f6c73ca06ed",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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