From 670a6f6c87f045a80017fb5bdf6911b4091a63ec Mon Sep 17 00:00:00 2001 From: JeniferVargas <88690404+JeniferVargas@users.noreply.github.com> Date: Sun, 10 Oct 2021 19:59:00 -0400 Subject: [PATCH] Add files via upload --- your-code/main.ipynb | 265 ++++++++++++++++++++++++++++++++++++++----- 1 file changed, 234 insertions(+), 31 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 95bfcb9..ed143e8 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -31,10 +31,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def bayes(priori, verosimilitud): \n", + " marginal=sum(np.multiply(priori, verosimilitud))\n", + " posteriori=np.divide(np.multiply(priori, verosimilitud), marginal)\n", + " return posteriori" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "prioris=[1/2, 1/2] # bolws de galletas\n", + "v_vainilla=[3/4, 2/4] # verosimilitud vainilla\n", + "v_chocolate=[1/4, 2/4] # verosimilitud chocolate" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probabilidad de que venga del cuenco de galletas 1: 0.6\n" + ] + } + ], + "source": [ + "print ('Probabilidad de que venga del cuenco de galletas 1: ',bayes(prioris, v_vainilla)[0])" + ] }, { "cell_type": "markdown", @@ -45,10 +78,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.4\n", + "0.4\n" + ] + } + ], + "source": [ + "print (bayes(prioris, v_vainilla)[1])\n", + "print (1-bayes(prioris, v_vainilla)[0])\n", + "#probabilidad de que venga del cuenco de galletas 2." + ] }, { "cell_type": "markdown", @@ -59,10 +105,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probabilidad de que venga del cuenco de galletas 1: 0.3333333333333333\n", + "Probabilidad de que venga del cuenco de galletas 2: 0.6666666666666666\n" + ] + } + ], + "source": [ + "print ('Probabilidad de que venga del cuenco de galletas 1:',bayes(prioris, v_chocolate)[0])\n", + "print ('Probabilidad de que venga del cuenco de galletas 2:',bayes(prioris, v_chocolate)[1])" + ] }, { "cell_type": "markdown", @@ -95,10 +153,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.5882352941176471" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prioris=[1/2, 1/2] # bags\n", + "\n", + "#verosimilitud\n", + "v_marron=[0.3, 0.13] \n", + "v_amarillo=[0.2, 0.14] \n", + "v_rojo=[0.2, 0.13] \n", + "v_verde=[0.1, 0.2] \n", + "v_naranja=[0.1, 0.16] \n", + "v_mandarina=[0.1, 0] \n", + "v_azul=[0, 0.24] \n", + "\n", + "v_amarillo[0]*prioris[0]/(v_verde[1]*prioris[1] + v_amarillo[1]*prioris[1])" + ] }, { "cell_type": "markdown", @@ -109,10 +191,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.411764705882353" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "v_amarillo[1]*prioris[1]/(v_verde[1]*prioris[1] + v_amarillo[1]*prioris[1])" + ] }, { "cell_type": "markdown", @@ -123,10 +219,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.3333333333333333" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v_verde[0]*(1/2)/(v_verde[0]*(1/2) + v_verde[1]*(1/2))" + ] }, { "cell_type": "markdown", @@ -141,10 +250,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Si se queda en puerta A, probabilidad: 0.3333333333333333\n", + "Si se cambia a puerta C, probabilidad: 0.6666666666666666\n" + ] + } + ], + "source": [ + "\n", + "prioris=[1/3, 1/3, 1/3] # puertas\n", + "v_premio=[0, 1/2, 1] # verosimilitud premio\n", + "\n", + "print ('Si se queda en puerta A, probabilidad:',bayes(prioris, v_premio)[1])\n", + "print ('Si se cambia a puerta C, probabilidad: ',bayes(prioris, v_premio)[2])" + ] }, { "cell_type": "markdown", @@ -157,10 +282,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "priori=pd.Series(np.random.uniform(0, 1, size=10000)) # distribucion a priori uniforme\n", + "sign_up=14 # 14 personas piden servicio\n", + "\n", + "\n", + "def modelo(param): # modelo binomial random\n", + " res=np.random.binomial(100, param) # se suponen 100 visitas\n", + " return res\n", + "\n", + "\n", + "datos=[modelo(p) for p in priori]\n", + "\n", + "posteriori=priori[list(map(lambda x: x==sign_up, datos))] # se genera el a posteriori\n", + "posteriori.hist()\n", + "plt.show()" + ] }, { "cell_type": "markdown", @@ -171,10 +324,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Se describe el a posteriori:\n", + "count 96.000000\n", + "mean 0.147349\n", + "std 0.036332\n", + "min 0.073259\n", + "25% 0.123798\n", + "50% 0.145745\n", + "75% 0.172073\n", + "max 0.266464\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "print ('Se describe el a posteriori:')\n", + "print (posteriori.describe())" + ] }, { "cell_type": "markdown", @@ -185,10 +358,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango intercuantil: 0.08714779708769099 | 0.20142887994197345\n" + ] + } + ], + "source": [ + "print('Rango intercuantil: ', posteriori.quantile(.05), '|', posteriori.quantile(.95)) \n", + "# rango intercuantil (90% de confianza)" + ] }, { "cell_type": "markdown", @@ -197,6 +381,25 @@ "What is the Maximum Likelihood Estimate?" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimación máximo-verosímil: 0.12 | 0.11458333333333333\n" + ] + } + ], + "source": [ + "modo=posteriori.round(2).mode()[0] #redondeo para maxima verosimilitud proporcion de visitantes...\n", + "prob=list(posteriori.round(2)).count(modo)/len(posteriori.round(2)) # ....con probabilidad \n", + "print('Estimación máximo-verosímil: ', modo, '|',prob)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -221,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.8.8" } }, "nbformat": 4,