From 3cdf47269268845be75d737530079083c19e1ccc Mon Sep 17 00:00:00 2001 From: Nathylyn <88753323+Nathylyn@users.noreply.github.com> Date: Tue, 5 Oct 2021 16:10:57 -0400 Subject: [PATCH 1/2] Add files via upload --- your-code/main.ipynb | 253 +++++++++++++++++++++++++++++++++++++------ 1 file changed, 220 insertions(+), 33 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 95bfcb9..f620378 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 +31,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def bayes(priori, verosimilitud): # regla de Bayes\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] # cuenco de galletas\n", + "\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": [ + "0.6\n" + ] + } + ], + "source": [ + "print (bayes(prioris, v_vainilla)[0])\n", + "# 0.6 => 60% de probabilidad de que venga del cuenco de galletas 1." + ] }, { "cell_type": "markdown", @@ -45,10 +80,23 @@ }, { "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", + "# 0.4 => 40% de probabilidad de que venga del cuenco de galletas 2." + ] }, { "cell_type": "markdown", @@ -59,10 +107,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.33333333 0.66666667]\n" + ] + } + ], + "source": [ + "print (bayes(prioris, v_chocolate))\n", + "# 0.333 => 33.3% de probabilidad de que venga del cuenco de galletas 1.\n", + "# 0.667 => 66.7% de probabilidad de que venga del cuenco de galletas 2." + ] }, { "cell_type": "markdown", @@ -95,10 +155,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7407407407407408\n" + ] + } + ], + "source": [ + "prioris=[1/2, 1/2] # bolsas\n", + "\n", + "v_marron=[0.3, 0.13] # verosimilitud marron\n", + "v_amarillo=[0.2, 0.14] # verosimilitud amarillo\n", + "v_rojo=[0.2, 0.13] # verosimilitud rojo\n", + "v_verde=[0.1, 0.2] # verosimilitud verde\n", + "v_naranja=[0.1, 0.16] # verosimilitud naranja\n", + "v_mandarina=[0.1, 0] # verosimilitud mandarina\n", + "v_azul=[0, 0.24] # verosimilitud azul\n", + "\n", + "\n", + "print (bayes(prioris, [v_amarillo[0]*v_verde[1], v_amarillo[1]*v_verde[0]])[0])\n", + "# 0.7407 => 74.1% de probabilidad de que la amarilla venga de la bolsa 1." + ] }, { "cell_type": "markdown", @@ -109,10 +191,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.25925925925925924\n" + ] + } + ], + "source": [ + "print (bayes(prioris, [v_amarillo[0]*v_verde[1], v_amarillo[1]*v_verde[0]])[1])\n", + "# 0.259 => 25.9% de probabilidad de que la amarilla venga de la bolsa 2." + ] }, { "cell_type": "markdown", @@ -123,10 +216,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#Las probabilidades en las verde son las complementarias de cada una,\n", + "#es decir, la probabilidad de que la verde sea de la bolsa 1 \n", + "#es 0.259 y la probabilidad de que la verde sea de la bolsa 2\n", + "#es 0.7407." + ] }, { "cell_type": "markdown", @@ -141,10 +239,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3333333333333333\n", + "0.6666666666666666\n" + ] + } + ], + "source": [ + "prioris=[1/3, 1/3, 1/3] # puertas\n", + "v_premio=[0, 1/2, 1] # verosimilitud del premio\n", + "\n", + "print (bayes(prioris, v_premio)[1])\n", + "# si no se cambia de puerta la probabilidad es 1/3\n", + "\n", + "print (bayes(prioris, v_premio)[2])\n", + "# si se cambia de puerta la probabilidad es 2/3" + ] }, { "cell_type": "markdown", @@ -157,10 +273,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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Vw5l54Nz0lvZfo2yZ+Up1fQp4tCvDWOy7ysjHXkT8BvBFYGdm/qjGukPdf2vkG/TYa5xvXMbfWvkqrYy/tku9zlcHPA78UXRcB5w59/ancjvL3n4sO+Z+G3BkgPn6WfdxYFd1exfw2LDzRUQADwJHM/P+ZfPa2H9Nsl0YEReduw18pCvDyPddl5GOvYj4ZeAAcEdmvlBz3aHtv7XyDWHsNc03FuNvnb/vOe2Mv438q2qdC52zW16g8y/Bf15N+yTwyep20PlxjZeARWCqa92fBX4EbFq2zX+sln2u2lFbBpjvF+j8X/d14LXq9s+ttW41/eeBp4AXq+tLh50P+F06b/eeAw5Xl5vb3H8Nsn2QztkA3wGeH7d9N0Zj74vA6a6/38J6645g/62abxhjr2G+cRl/6/19Wxt/fk2AJBXET5RKUkEsdUkqiKUuSQWx1CWpIJa6JBXEUpekgljqklSQ/weZ8SMM4BKFaAAAAABJRU5ErkJggg==\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 +315,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Se describe el a posteriori:\n", + "count 98.000000\n", + "mean 0.146408\n", + "std 0.035133\n", + "min 0.081052\n", + "25% 0.122832\n", + "50% 0.143073\n", + "75% 0.166139\n", + "max 0.271698\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "print ('Se describe el a posteriori:')\n", + "print (posteriori.describe())" + ] }, { "cell_type": "markdown", @@ -185,10 +349,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango intercuantil: 0.09785696449580149 | 0.20233792671069697\n" + ] + } + ], + "source": [ + "print('Rango intercuantil: ', posteriori.quantile(.05), '|', posteriori.quantile(.95)) \n", + "# rango intercuantil (90% de confianza)" + ] }, { "cell_type": "markdown", @@ -199,10 +374,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimación máximo-verosímil: 0.14 | 0.15306122448979592\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)" + ] } ], "metadata": { @@ -221,7 +408,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.8.8" } }, "nbformat": 4, From f09b50a5fb47b25acb3e58fab24dbc8df1d63d76 Mon Sep 17 00:00:00 2001 From: Nathylyn <88753323+Nathylyn@users.noreply.github.com> Date: Thu, 7 Oct 2021 18:22:30 -0400 Subject: [PATCH 2/2] Add files via upload --- your-code/main.ipynb | 113 +++++++++++++++++++++++-------------------- 1 file changed, 60 insertions(+), 53 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index f620378..5e8d2bb 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -35,7 +35,7 @@ "metadata": {}, "outputs": [], "source": [ - "def bayes(priori, verosimilitud): # regla de Bayes\n", + "def bayes(priori, verosimilitud): \n", " marginal=sum(np.multiply(priori, verosimilitud))\n", " posteriori=np.divide(np.multiply(priori, verosimilitud), marginal)\n", " return posteriori" @@ -47,28 +47,26 @@ "metadata": {}, "outputs": [], "source": [ - "prioris=[1/2, 1/2] # cuenco de galletas\n", - "\n", + "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, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.6\n" + "Probabilidad de que venga del cuenco de galletas 1: 0.6\n" ] } ], "source": [ - "print (bayes(prioris, v_vainilla)[0])\n", - "# 0.6 => 60% de probabilidad de que venga del cuenco de galletas 1." + "print ('Probabilidad de que venga del cuenco de galletas 1: ',bayes(prioris, v_vainilla)[0])\n" ] }, { @@ -95,7 +93,7 @@ "source": [ "print (bayes(prioris, v_vainilla)[1])\n", "print (1-bayes(prioris, v_vainilla)[0])\n", - "# 0.4 => 40% de probabilidad de que venga del cuenco de galletas 2." + "#probabilidad de que venga del cuenco de galletas 2." ] }, { @@ -107,21 +105,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.33333333 0.66666667]\n" + "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 (bayes(prioris, v_chocolate))\n", - "# 0.333 => 33.3% de probabilidad de que venga del cuenco de galletas 1.\n", - "# 0.667 => 66.7% de probabilidad de que venga del cuenco de galletas 2." + "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])" ] }, { @@ -155,31 +153,33 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 51, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7407407407407408\n" - ] + "data": { + "text/plain": [ + "0.5882352941176471" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "prioris=[1/2, 1/2] # bolsas\n", + "prioris=[1/2, 1/2] # bags\n", "\n", - "v_marron=[0.3, 0.13] # verosimilitud marron\n", - "v_amarillo=[0.2, 0.14] # verosimilitud amarillo\n", - "v_rojo=[0.2, 0.13] # verosimilitud rojo\n", - "v_verde=[0.1, 0.2] # verosimilitud verde\n", - "v_naranja=[0.1, 0.16] # verosimilitud naranja\n", - "v_mandarina=[0.1, 0] # verosimilitud mandarina\n", - "v_azul=[0, 0.24] # verosimilitud azul\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", - "\n", - "print (bayes(prioris, [v_amarillo[0]*v_verde[1], v_amarillo[1]*v_verde[0]])[0])\n", - "# 0.7407 => 74.1% de probabilidad de que la amarilla venga de la bolsa 1." + "v_amarillo[0]*prioris[0]/(v_verde[1]*prioris[1] + v_amarillo[1]*prioris[1])\n" ] }, { @@ -191,20 +191,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 53, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.25925925925925924\n" - ] + "data": { + "text/plain": [ + "0.411764705882353" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print (bayes(prioris, [v_amarillo[0]*v_verde[1], v_amarillo[1]*v_verde[0]])[1])\n", - "# 0.259 => 25.9% de probabilidad de que la amarilla venga de la bolsa 2." + "v_amarillo[1]*prioris[1]/(v_verde[1]*prioris[1] + v_amarillo[1]*prioris[1])\n" ] }, { @@ -216,14 +218,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.3333333333333333" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#Las probabilidades en las verde son las complementarias de cada una,\n", - "#es decir, la probabilidad de que la verde sea de la bolsa 1 \n", - "#es 0.259 y la probabilidad de que la verde sea de la bolsa 2\n", - "#es 0.7407." + "v_verde[0]*(1/2)/(v_verde[0]*(1/2) + v_verde[1]*(1/2))" ] }, { @@ -239,27 +249,24 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.3333333333333333\n", - "0.6666666666666666\n" + "Si se queda en puerta A, probabilidad: 0.3333333333333333\n", + "Si se cambia a puerta C, probabilidad: 0.6666666666666666\n" ] } ], "source": [ "prioris=[1/3, 1/3, 1/3] # puertas\n", - "v_premio=[0, 1/2, 1] # verosimilitud del premio\n", - "\n", - "print (bayes(prioris, v_premio)[1])\n", - "# si no se cambia de puerta la probabilidad es 1/3\n", + "v_premio=[0, 1/2, 1] # verosimilitud premio\n", "\n", - "print (bayes(prioris, v_premio)[2])\n", - "# si se cambia de puerta la probabilidad es 2/3" + "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]) \n" ] }, {