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‎10_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": 2,
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"metadata": {
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"collapsed": true
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},
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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": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.11.2'"
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]
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},
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"execution_count": 3,
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"__author__ = 'kyubyong. [email protected]'"
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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": 49,
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"metadata": {
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"collapsed": false
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},
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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([[ 0.13879034, 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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"execution_count": 49,
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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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},
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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": 42,
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"metadata": {
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"collapsed": false
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},
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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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"-0.00110028519551\n",
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"0.999683483393\n"
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]
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}
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],
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"source": []
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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": 44,
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"metadata": {
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"collapsed": false
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},
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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, 3],\n",
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" [3, 0],\n",
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" [0, 0]])"
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]
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},
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"execution_count": 44,
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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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},
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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": 3,
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"metadata": {
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"collapsed": false
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},
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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 out of 10\n",
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"2 out of 10\n",
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"3 out of 10\n",
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"5 out of 10\n",
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"2 out of 10\n",
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"5 out of 10\n",
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"2 out of 10\n",
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"2 out of 10\n",
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"2 out of 10\n",
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"5 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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]
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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": 66,
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"metadata": {
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"collapsed": false
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},
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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([5, 4, 0])"
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]
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},
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"execution_count": 66,
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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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},
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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": 86,
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"metadata": {
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"collapsed": false
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},
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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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"[2 3 8 4 5 1 0 6 9 7]\n"
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]
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}
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],
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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": 88,
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"metadata": {
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"collapsed": false
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},
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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": 91,
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"metadata": {
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"collapsed": true
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},
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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 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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