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replace print->print() for python3.x #11

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replace print->print() for python3.x #11

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338 changes: 1 addition & 337 deletions 10_Random_sampling_Solutions.ipynb
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Original file line number Diff line number Diff line change
@@ -1,337 +1 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Random Sampling"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'1.11.2'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.__version__"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"__author__ = 'kyubyong. [email protected]'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple random data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1)."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.13879034, 0.71300174],\n",
" [ 0.08121322, 0.00393554],\n",
" [ 0.02349471, 0.56677474]])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(3, 2) \n",
"# Or np.random.random((3,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.00110028519551\n",
"0.999683483393\n"
]
}
],
"source": [
"out1 = np.random.randn(1000, 1000)\n",
"out2 = np.random.standard_normal((1000, 1000))\n",
"out3 = np.random.normal(loc=0.0, scale=1.0, size=(1000, 1000))\n",
"assert np.allclose(np.mean(out1), np.mean(out2), atol=0.1)\n",
"assert np.allclose(np.mean(out1), np.mean(out3), atol=0.1)\n",
"assert np.allclose(np.std(out1), np.std(out2), atol=0.1)\n",
"assert np.allclose(np.std(out1), np.std(out3), atol=0.1)\n",
"print np.mean(out3)\n",
"print np.std(out1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 3],\n",
" [3, 0],\n",
" [0, 0]])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(0, 4, (3, 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 out of 10\n",
"5 out of 10\n",
"3 out of 10\n",
"5 out of 10\n",
"5 out of 10\n",
"5 out of 10\n",
"2 out of 10\n",
"2 out of 10\n",
"5 out of 10\n",
"5 out of 10\n"
]
}
],
"source": [
"for _ in range(10):\n",
" print np.random.choice(x, p=[.3, .5, .2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([5, 4, 0])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.choice(10, 3, replace=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Permutations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q6. Shuffle numbers between 0 and 9 (inclusive)."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2 3 8 4 5 1 0 6 9 7]\n"
]
}
],
"source": [
"x = np.arange(10)\n",
"np.random.shuffle(x)\n",
"print x"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5 2 7 4 1 0 6 8 9 3]\n"
]
}
],
"source": [
"# Or\n",
"print np.random.permutation(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random generator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Random Sampling"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":"import numpy as np"},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"data":{"text/plain":"'1.16.5'"},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":"np.__version__"},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":"__author__ = 'kyubyong. [email protected]'"},{"cell_type":"markdown","metadata":{},"source":["## Simple random data"]},{"cell_type":"markdown","metadata":{},"source":["Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1)."]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":"array([[0.91619879, 0.84402355],\n [0.96187101, 0.63032521],\n [0.87581479, 0.49324904]])"},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":"np.random.rand(3, 2) \n# Or np.random.random((3,2))"},{"cell_type":"markdown","metadata":{},"source":["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."]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"-0.0007331580720093552\n1.0006913189352846\n"}],"source":"out1 = np.random.randn(1000, 1000)\nout2 = np.random.standard_normal((1000, 1000))\nout3 = np.random.normal(loc=0.0, scale=1.0, size=(1000, 1000))\nassert np.allclose(np.mean(out1), np.mean(out2), atol=0.1)\nassert np.allclose(np.mean(out1), np.mean(out3), atol=0.1)\nassert np.allclose(np.std(out1), np.std(out2), atol=0.1)\nassert np.allclose(np.std(out1), np.std(out3), atol=0.1)\nprint (np.mean(out3))\nprint (np.std(out1))\n"},{"cell_type":"markdown","metadata":{},"source":["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."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/plain":"array([[2, 0],\n [3, 1],\n [2, 0]])"},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":"np.random.randint(0, 4, (3, 2))"},{"cell_type":"markdown","metadata":{},"source":["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."]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":"x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']"},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"b'3 out of 10'\nb'5 out of 10'\nb'3 out of 10'\nb'5 out of 10'\nb'2 out of 10'\nb'5 out of 10'\nb'2 out of 10'\nb'5 out of 10'\nb'5 out of 10'\nb'5 out of 10'\n"}],"source":"for _ in range(10):\n print (np.random.choice(x, p=[.3, .5, .2]))"},{"cell_type":"markdown","metadata":{},"source":["Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities."]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":"array([5, 8, 3])"},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":"np.random.choice(10, 3, replace=False)"},{"cell_type":"markdown","metadata":{},"source":["## Permutations"]},{"cell_type":"markdown","metadata":{},"source":["Q6. Shuffle numbers between 0 and 9 (inclusive)."]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"[0 8 9 5 7 4 2 6 1 3]\n"}],"source":"x = np.arange(10)\nnp.random.shuffle(x)\nprint (x)"},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"[9 6 3 1 5 2 0 8 7 4]\n"}],"source":"# Or\nprint (np.random.permutation(10))"},{"cell_type":"markdown","metadata":{},"source":["## Random generator"]},{"cell_type":"markdown","metadata":{},"source":["Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time."]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":"np.random.seed(10)"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3}},"orig_nbformat":2,"file_extension":".py","mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3}}
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