From 98b9462b9ab02b435c225788652a282199b7dc13 Mon Sep 17 00:00:00 2001 From: Jeremy Teitelbaum Date: Sun, 15 Apr 2018 11:48:50 -0400 Subject: [PATCH] work on 3.10.11 --- BDA 3.10.11.ipynb | 86 ++++++++++++++++++++--------------------------- gelman.r | 4 ++- 2 files changed, 40 insertions(+), 50 deletions(-) diff --git a/BDA 3.10.11.ipynb b/BDA 3.10.11.ipynb index 4c37fc8..62dec8a 100644 --- a/BDA 3.10.11.ipynb +++ b/BDA 3.10.11.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -60,30 +60,30 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.599274021823669\n", - "[0.96947339 1.55234798 2.69693603]\n" + "2.6356753062363643\n", + "[0.95549603 1.54734748 2.68231072]\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -129,12 +129,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -205,14 +205,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/jteitelbaum/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log\n", + "/home/jet08013/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log\n", " \n" ] }, @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -249,7 +249,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jteitelbaum/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n", + "/home/jet08013/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n", " from ipykernel import kernelapp as app\n" ] }, @@ -282,64 +282,52 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-2. , -2. , -2. , ..., -2. ,\n", - " -2. , -2. ],\n", - " [-1.9798995 , -1.9798995 , -1.9798995 , ..., -1.9798995 ,\n", - " -1.9798995 , -1.9798995 ],\n", - " [-1.95979899, -1.95979899, -1.95979899, ..., -1.95979899,\n", - " -1.95979899, -1.95979899],\n", - " ...,\n", - " [ 1.95979899, 1.95979899, 1.95979899, ..., 1.95979899,\n", - " 1.95979899, 1.95979899],\n", - " [ 1.9798995 , 1.9798995 , 1.9798995 , ..., 1.9798995 ,\n", - " 1.9798995 , 1.9798995 ],\n", - " [ 2. , 2. , 2. , ..., 2. ,\n", - " 2. , 2. ]])" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "Y\n", + "u=list(map(sum,Z2))\n", "\n" ] }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.82616995 1.33837647 1.80934486 2.49570902 5.68990452]\n" + "1.437043700373185 [0.68945124 0.99000008 1.26005551 1.65286899 3.30679651]\n" ] } ], "source": [ "Psigma=list(map(sum,np.exp(Z2-np.max(Z2))))\n", "p=Psigma/sum(Psigma)\n", - "s=np.random.choice(np.exp(np.linspace(-2,2,200)),p=p,size=1000)\n", - "print(np.percentile(s,q=[2.5,25,50,75,97.5]))" + "s=np.random.choice(np.exp(np.linspace(-2,4,200)),p=p,size=10000)\n", + "print(np.mean(s),np.percentile(s,q=[2.5,25,50,75,97.5]))" ] }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.371512604377929 [0.61111977 0.9320659 1.18631786 1.60377754 3.20858216]\n" + ] + } + ], "source": [ - "p=[1/3,1/3].append([1/200.0]*198)" + "Psigma=list(map(sum,np.exp(Z1-np.max(Z1))))\n", + "p=Psigma/sum(Psigma)\n", + "s=np.random.choice(np.exp(np.linspace(-2,4,200)),p=p,size=10000)\n", + "print(np.mean(s),np.percentile(s,q=[2.5,25,50,75,97.5]))" ] }, { @@ -595,7 +583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/gelman.r b/gelman.r index f39d03f..dcfcd05 100644 --- a/gelman.r +++ b/gelman.r @@ -33,4 +33,6 @@ ldens} mu <- mugrid[muindex] sd <- rep (NA,nsim) for (i in (1:nsim)) sd[i] <- exp (sample(logsdgrid, 1, prob=dens[muindex[i],])) - kprint (rbind (summ(mu),summ(sd))) \ No newline at end of file + print (rbind (summ(mu),summ(sd))) + +