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‎Random_sampling.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Random Sampling"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.14.3'"
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]
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},
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"execution_count": 2,
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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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"np.__version__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"__author__ = 'kyubyong. longinglove@nate.com'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simple random data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.55289251, 0.73751705],\n",
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" [0.09262449, 0.46330727],\n",
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" [0.93533683, 0.27820819]])"
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]
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},
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"execution_count": 6,
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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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"np.random.rand(3,2)\n",
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"# array([[ 0.13879034, 0.71300174],\n",
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"# [ 0.08121322, 0.00393554],\n",
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"# [ 0.02349471, 0.56677474]])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q2. Create an array of shape (1000, 1000) and populate it with random samples from a standard normal distribution. And verify that the mean and standard deviation is close enough to 0 and 1 repectively."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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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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"0.0028843956793259185\n",
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"1.0004664449591625\n"
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]
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}
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],
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"source": [
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"x = np.random.standard_normal(size=(1000,1000))\n",
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"print(np.mean(x))\n",
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"print(np.std(x))\n",
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"# -0.00110028519551\n",
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"# 0.999683483393"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q3. Create an array of shape (3, 2) and populate it with random integers ranging from 0 to 3 (inclusive) from a discrete uniform distribution."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 0],\n",
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" [0, 3],\n",
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" [2, 1]])"
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]
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},
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"execution_count": 22,
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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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"np.random.randint(0,4,size=(3,2))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q4. Extract 1 elements from x randomly such that each of them would be associated with probabilities .3, .5, .2. Then print the result 10 times."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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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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"b'5 out of 10'\n",
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"b'5 out of 10'\n",
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"b'5 out of 10'\n",
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"b'2 out of 10'\n",
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"b'2 out of 10'\n",
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"b'5 out of 10'\n",
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"b'3 out of 10'\n",
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"b'3 out of 10'\n",
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"b'2 out of 10'\n",
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"b'3 out of 10'\n"
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]
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}
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],
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"source": [
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"x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']\n",
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"for i in range(10):\n",
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" print(np.random.choice(x,p=[0.3, 0.5,0.2]))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([2, 7, 5])"
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]
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},
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"execution_count": 37,
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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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"np.random.choice(range(10),size=3)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Permutations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q6. Shuffle numbers between 0 and 9 (inclusive)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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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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"[9 8 4 5 6 1 7 2 3 0]\n"
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]
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}
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],
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"source": [
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"x = np.arange(10)\n",
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"np.random.shuffle(x)\n",
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"print(x)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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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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"[5 2 7 4 1 0 6 8 9 3]\n"
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]
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}
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],
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"source": [
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"# Or\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Random generator"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([4.17022005e-01, 7.20324493e-01, 1.14374817e-04])"
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]
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},
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"execution_count": 61,
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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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"np.random.seed(1)\n",
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"np.random.rand(2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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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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"cell_type": "code",
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"execution_count": null,
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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",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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