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265 changes: 234 additions & 31 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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",
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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,
Expand All @@ -221,7 +424,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.8.8"
}
},
"nbformat": 4,
Expand Down