221 lines
4.4 KiB
Plaintext
221 lines
4.4 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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"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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"from network.gibbs import *\n",
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"from network.network import pp_network, can_antenna_be_turned_on\n",
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"from collections import defaultdict\n",
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"from random import Random\n",
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"from numpy import matrix"
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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": false
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},
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"outputs": [],
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"source": [
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"mc = defaultdict(dict)\n",
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"random = Random(66)"
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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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"source": [
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"prec_network = 0\n",
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"\n",
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"for i, network in zip(range(10**5), get_random_networks(2, random)):\n",
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" if network not in mc[prec_network]:\n",
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" mc[prec_network][network] = 0\n",
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" mc[prec_network][network] += 1\n",
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" prec_network = network"
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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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"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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"defaultdict(dict,\n",
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" {0: {0: 964, 1: 3671, 2: 1844, 4: 1759, 6: 1835, 8: 938, 9: 3387},\n",
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" 1: {0: 891, 1: 3646, 2: 1832, 4: 1839, 6: 1729, 8: 938, 9: 3663},\n",
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" 2: {0: 1810, 2: 3548, 4: 3517, 6: 3611, 8: 1811},\n",
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" 4: {0: 1779, 2: 3519, 4: 3490, 6: 3486, 8: 1796},\n",
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" 6: {0: 1885, 2: 3554, 4: 3465, 6: 3475, 8: 1757},\n",
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" 8: {0: 3633, 1: 3553, 8: 3654, 9: 3572},\n",
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" 9: {0: 3436, 1: 3668, 8: 3518, 9: 3527}})"
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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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"mc"
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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": 4,
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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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"def get_prob(from_, to):\n",
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" return mc[from_][to]/ sum(mc[from_].values())\n",
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"\n",
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"def get_matrix(mc):\n",
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" n = len(mc)\n",
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" mat = [[] for i in range(n)]\n",
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" for i, from_ in enumerate(mc):\n",
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" for j in enumerate(mc):\n",
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" pass\n",
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" "
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"for key in mc:\n",
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" print(key)\n",
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" print( pp_network(key, 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": 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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"□-□\n",
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"■-□\n",
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"\n",
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"1 True\n"
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]
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}
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],
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"source": [
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"ant = 1\n",
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"print(get_prob(0, 2))\n",
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"print(ant, can_antenna_be_turned_on(ant, 4, 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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"0.5**4"
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"m = matrix([[1, 2], [3, 4]])\n",
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"v = matrix([[1, 3]])"
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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": 8,
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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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"from numpy import std"
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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": 10,
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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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"42.73458667121028"
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]
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},
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"execution_count": 10,
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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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"std([1432, 1376, 1461, 1436, 1436, 1363, 1496])"
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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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"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 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": "3.4.3+"
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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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