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‎BayesianNetworks.ipynb

+45-24
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"cell_type": "code",
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"execution_count": 1,
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"cell_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 11,
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"metadata": {
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"scrolled": false
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 12,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{('No', 'Negative'): 0.050847587999999999,\n",
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" ('No', 'Positive'): 0.0039778200000000008,\n",
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" ('Yes', 'Negative'): 0.00049286000000000002,\n",
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" ('Yes', 'Positive'): 0.00020090000000000003}"
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"{('Negative',): 0.051340447999999997, ('Positive',): 0.0041787200000000012}"
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]
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},
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"execution_count": 38,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"eng.propagate_observation({'X': 'Positive', 'D':'Positive', 'S':'Yes'})\n",
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"#normalize(eng.compute_marginal(['T']))\n",
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"eng.marginal_table(['A','T'])"
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"eng.marginal_table(['T'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{('Negative',): 0.051340447999999997, ('Positive',): 0.0041787200000000012}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"eng.marginal_table(['T'])"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 27,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'B': {'Negative': 0.29908037361623613, 'Positive': 0.70091962638376382},\n",
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"{'A': {'Yes': 1.0},\n",
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" 'B': {'Negative': 0.29908037361623618, 'Positive': 0.70091962638376382},\n",
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" 'L': {'Negative': 0.42083717712177127, 'Positive': 0.57916282287822873},\n",
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" 'T': {'Negative': 0.71041858856088558, 'Positive': 0.28958141143911437}}"
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" 'T': {'Negative': 0.71041858856088558, 'Positive': 0.28958141143911442}}"
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]
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},
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"execution_count": 42,
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#eng.propagate_observation({'X': 'Positive', 'D':'Positive', 'S':'Yes'})\n",
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"eng.propagate_observation({'A':'Yes','S':'Yes','X':'Positive','D':'Positive'})\n",
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"#eng.propagate_observation({'A':'Yes','S':'Yes','X':'Positive','D':'Positive'})\n",
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"#eng.propagate_observation({'A':'Yes','S':'Yes'})\n",
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"eng.singleton_marginals(['T','L','B'], normalization=True)"
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"eng.propagate_observation({'X': 'Positive', 'A':'Yes', 'D':'Positive','S':'Yes'})\n",
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"#eng.propagate_observation({'S':'Yes'})\n",
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"#eng.propagate_observation({})\n",
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"eng.singleton_marginals(['A','T','L','B'], normalization=True)"
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]
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},
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{

‎ModelingExamples2.ipynb

+15-15
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@@ -387,7 +387,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"metadata": {
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"scrolled": false
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"M1: -193.727948747\n",
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"M2: -170.948939201\n",
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"M3: -170.948939201\n",
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"M3b: -170.948939201\n",
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"M1: -25.8064501902\n",
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"M2: -24.7950874021\n",
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"M3: -24.7950874021\n",
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"M3b: -24.7950874021\n",
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"Log Odds, M1-M2\n",
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"-22.7790095463\n",
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"[ 1.27997607e-10 1.00000000e+00]\n",
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"-1.01136278809\n",
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"[ 0.26671324 0.73328676]\n",
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"Log Odds, M1-M3\n",
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"-22.7790095463\n",
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"[ 1.27997607e-10 1.00000000e+00]\n",
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"-1.01136278809\n",
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"[ 0.26671324 0.73328676]\n",
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"Log Odds, M1-M3b\n",
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"-22.7790095463\n",
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"[ 1.27997607e-10 1.00000000e+00]\n"
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"-1.01136278809\n",
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"[ 0.26671324 0.73328676]\n"
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]
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}
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],
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"import scipy as sc\n",
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"from scipy.special import gammaln\n",
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"\n",
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"C = np.array([[3,1,9],[7,9,17]])\n",
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"#C = 1*np.array([[1,1,3],[1,1,7]])\n",
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"#C = np.array([[3,1,9],[7,9,17]])\n",
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"C = 1*np.array([[4,1,1],[1,1,6]])\n",
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"\n",
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"#C = np.array([[0,1,1],[1,0,2]])\n",
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"\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.3"
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"version": "3.6.1"
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},
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"toc": {
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"nav_menu": {},

‎TextProcessing.ipynb

+5-5
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