{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import numpy.random as rnd\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as stats" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "def lancio(prec=[[]]):\n", " res=[l+['T'] for l in prec] + [l+['C'] for l in prec]\n", " return res" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[['T'], ['C']]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lancio()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[['T', 'T'], ['C', 'T'], ['T', 'C'], ['C', 'C']]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lancio(lancio())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[['T', 'T', 'T', 'T'],\n", " ['C', 'T', 'T', 'T'],\n", " ['T', 'C', 'T', 'T'],\n", " ['C', 'C', 'T', 'T'],\n", " ['T', 'T', 'C', 'T'],\n", " ['C', 'T', 'C', 'T'],\n", " ['T', 'C', 'C', 'T'],\n", " ['C', 'C', 'C', 'T'],\n", " ['T', 'T', 'T', 'C'],\n", " ['C', 'T', 'T', 'C'],\n", " ['T', 'C', 'T', 'C'],\n", " ['C', 'C', 'T', 'C'],\n", " ['T', 'T', 'C', 'C'],\n", " ['C', 'T', 'C', 'C'],\n", " ['T', 'C', 'C', 'C'],\n", " ['C', 'C', 'C', 'C']]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lanci=lancio()\n", "for i in range(3):\n", " lanci=lancio(lanci)\n", "lanci\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[4, 3, 3, 2, 3, 2, 2, 1, 3, 2, 2, 1, 2, 1, 1, 0]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x.count('T') for x in lanci]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x.count('T') for x in lanci].count(3)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x.count('T') for x in lanci].count(1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 2, 1, 0, 1, 2, 2, 1, 2, 1, 1, 0, 2, 2, 1, 3, 1, 0, 1])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd.binomial(4,2/6,20)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3486784401000001 0.3874204890000001 0.7360989291000002\n" ] } ], "source": [ "p10=0.9**10\n", "p9=10*0.1*0.9**9\n", "print(p10, p9, p9+p10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([ 5, 10, 10, 9, 10, 8, 10, 7, 9, 10, 8, 7, 9, 10, 9, 9, 10,\n", " 7, 10, 8])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd.binomial(10,0.9,20)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "79" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test=rnd.binomial(10,0.9,100)\n", "np.sum(test > 8)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [], "source": [ "def lancio_dadi(prec=[[]]):\n", " res=[l+[x] for l in prec for x in range(1,7)]\n", " return res\n", "\n", "def lancio_n(n):\n", " res=lancio_dadi()\n", " for i in range(n-1):\n", " res=lancio_dadi(res)\n", " return res" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[[1, 1],\n", " [1, 2],\n", " [1, 3],\n", " [1, 4],\n", " [1, 5],\n", " [1, 6],\n", " [2, 1],\n", " [2, 2],\n", " [2, 3],\n", " [2, 4],\n", " [2, 5],\n", " [2, 6],\n", " [3, 1],\n", " [3, 2],\n", " [3, 3],\n", " [3, 4],\n", " [3, 5],\n", " [3, 6],\n", " [4, 1],\n", " [4, 2],\n", " [4, 3],\n", " [4, 4],\n", " [4, 5],\n", " [4, 6],\n", " [5, 1],\n", " [5, 2],\n", " [5, 3],\n", " [5, 4],\n", " [5, 5],\n", " [5, 6],\n", " [6, 1],\n", " [6, 2],\n", " [6, 3],\n", " [6, 4],\n", " [6, 5],\n", " [6, 6]]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lancio_n(2)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [], "source": [ "def histo_dadi(dadi):\n", " plt.hist(dadi, bins=np.arange(0.5,36.5))\n", "\n", "def somma_dadi(dadi):\n", " return [sum(x) for x in dadi]\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histo_dadi(somma_dadi(lancio_n(2)))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histo_dadi(somma_dadi(lancio_n(3)))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histo_dadi(somma_dadi(lancio_n(4)))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([4, 6, 4, 3, 4, 6, 4, 5, 4, 3, 3, 4, 6, 4, 6, 2, 3, 5, 1, 4])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd.randint(1,7,20)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[6, 1, 1],\n", " [3, 5, 1],\n", " [6, 5, 4]])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd.randint(1,7,(3,3))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dd=rnd.randint(1,7,(10000,6))\n", "histo_dadi(somma_dadi(dd))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 4 }