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Mario Chirinos Colunga
tap1012
Commits
fcbca2e1
Commit
fcbca2e1
authored
Mar 23, 2019
by
Carlos David García Hernández
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Examen 2
parent
3f212f40
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123 additions
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+123
-14
08-Test2-checkpoint.ipynb
.ipynb_checkpoints/08-Test2-checkpoint.ipynb
+89
-4
08-Test2.ipynb
08-Test2.ipynb
+34
-10
No files found.
.ipynb_checkpoints/08-Test2-checkpoint.ipynb
View file @
fcbca2e1
...
...
@@ -566,7 +566,7 @@
"cell_type": "code",
"execution_count": 202,
"metadata": {
"scrolled":
tru
e
"scrolled":
fals
e
},
"outputs": [
{
...
...
@@ -785,13 +785,98 @@
},
{
"cell_type": "code",
"execution_count":
203
,
"execution_count":
35
,
"metadata": {},
"outputs": [],
"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"
},
{
"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": [
"import json\n",
"import re\n",
"from scipy import stats\n",
"from scipy.stats import norm\n",
"from collections import OrderedDict\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"with open('data/Exa2.json') as file:\n",
" data = json.load(file)\n",
"\n",
"exa2="
"conjunto=[]\n",
"worked=[]\n",
"worked1=[]\n",
"dicti={}\n",
"omitir=[\"<START:Location>\",\"<END>\",\"<end>\",\"<start\",\"location>\"]\n",
"\n",
"for texto in range(1,len(data),1):\n",
" conjunto.append(data[texto][\"doc_locations\"])\n",
"\n",
"for texto in conjunto:\n",
" texto=re.sub(r\"[,| \\. | :|?|=|«|»| –|(|)]\",\" \",texto)\n",
" texto=texto.lower()\n",
" worked.append(texto)\n",
"\n",
"for texto in range(1,len(worked),1):\n",
" worked1=worked[texto].split()\n",
" for palabra in worked1:\n",
" #print(palabra)\n",
" if palabra not in dicti.keys():\n",
" dicti.update({palabra:1})\n",
" elif palabra in dicti.keys():\n",
" dicti.update({palabra:dicti[str(palabra)]+1})\n",
" \n",
"for nop in omitir:\n",
" if nop in dicti.keys():\n",
" del(dicti[nop])\n",
"\n",
" \n",
"dictiOrd=OrderedDict(sorted(dicti.items(), key=lambda t: t[1],reverse=True))\n",
"#print(dictiOrd)\n",
"val1=[]\n",
"nom1=[]\n",
"vals = np.fromiter(dictiOrd.values(), dtype=float)\n",
"nombres=list(dictiOrd.keys())\n",
"\n",
"hist=np.histogram(vals, bins=100)\n",
"hist_dist = stats.rv_histogram(hist)\n",
"mediaF=np.mean(vals)\n",
"stdF=np.std(vals)\n",
"\n",
"F = np.random.normal(mediaF, stdF, 500)\n",
"count, bins, ignored = plt.hist(F, 20, normed=True,color=\"blue\",label=\"Histograma\", alpha=0.5)\n",
"v=plt.plot(bins, 1/(stdF * np.sqrt(2 * np.pi)) *np.exp(- (bins - mediaF)**2 / (2 * stdF**2) ),linewidth=5, color='r',label=\"Gauss\")\n",
"plt.ylabel('Probabilidad')\n",
"plt.legend(fancybox=True)\n",
"plt.show()\n",
"\n",
"X = np.linspace(-5.0, 5.0, 10000)\n",
"plt.title(\"Histograma\")\n",
"plt.hist(vals, density=True, bins=100)\n",
"plt.plot(X, hist_dist.pdf(X))\n",
"plt.show()"
]
}
],
...
...
08-Test2.ipynb
View file @
fcbca2e1
...
...
@@ -785,9 +785,34 @@
},
{
"cell_type": "code",
"execution_count":
null
,
"execution_count":
35
,
"metadata": {},
"outputs": [],
"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"
},
{
"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": [
"import json\n",
"import re\n",
...
...
@@ -837,22 +862,21 @@
"\n",
"hist=np.histogram(vals, bins=100)\n",
"hist_dist = stats.rv_histogram(hist)\n",
"print(len(hist_dist))\n",
"mediaF=np.mean(vals)\n",
"stdF=np.std(vals)\n",
"\n",
"
#
F = np.random.normal(mediaF, stdF, 500)\n",
"
#
count, bins, ignored = plt.hist(F, 20, normed=True,color=\"blue\",label=\"Histograma\", alpha=0.5)\n",
"
#
v=plt.plot(bins, 1/(stdF * np.sqrt(2 * np.pi)) *np.exp(- (bins - mediaF)**2 / (2 * stdF**2) ),linewidth=5, color='r',label=\"Gauss\")\n",
"
#
plt.ylabel('Probabilidad')\n",
"
#
plt.legend(fancybox=True)\n",
"
#
plt.show()\n",
"F = np.random.normal(mediaF, stdF, 500)\n",
"count, bins, ignored = plt.hist(F, 20, normed=True,color=\"blue\",label=\"Histograma\", alpha=0.5)\n",
"v=plt.plot(bins, 1/(stdF * np.sqrt(2 * np.pi)) *np.exp(- (bins - mediaF)**2 / (2 * stdF**2) ),linewidth=5, color='r',label=\"Gauss\")\n",
"plt.ylabel('Probabilidad')\n",
"plt.legend(fancybox=True)\n",
"plt.show()\n",
"\n",
"X = np.linspace(-5.0, 5.0, 10000)\n",
"plt.title(\"Histograma\")\n",
"plt.hist(vals, density=True, bins=100)\n",
"plt.plot(X, hist_dist.pdf(X))\n",
"
#
plt.show()"
"plt.show()"
]
}
],
...
...
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