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"!date"
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"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Wed Mar 6 12:56:23 PST 2013\r\n"
]
}
],
"prompt_number": 1
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{
"cell_type": "code",
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"import ipynb_style\n",
"ipynb_style.clean()"
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{
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"metadata": {},
"source": [
"Stan is not an acronym, it is a BUGS-competitor MCMC package produced by Andrew Gelman and company. It has come up enough now that I need to give it a try. There is also some enthusiasm about packaging it for python, a la rstan.\n",
"\n",
"http://mc-stan.org/\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!wget https://stan.googlecode.com/files/stan-src-1.1.1.tgz"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"--2013-03-06 13:00:09-- https://stan.googlecode.com/files/stan-src-1.1.1.tgz\r\n",
"Resolving stan.googlecode.com... 74.125.141.82, 2607:f8b0:400e:c02::52\r\n",
"Connecting to stan.googlecode.com|74.125.141.82|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
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"text": [
"200 OK\r\n",
"Length: 24059353 (23M) [application/x-gzip]\r\n",
"Saving to: \u201cstan-src-1.1.1.tgz\u201d\r\n",
"\r\n",
"\r",
" 0% [ ] 0 --.-K/s "
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"99% [=====================================> ] 23,921,627 7.57M/s eta 0s \r",
"100%[======================================>] 24,059,353 7.61M/s in 3.0s \r\n",
"\r\n"
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"text": [
"2013-03-06 13:00:12 (7.61 MB/s) - \u201cstan-src-1.1.1.tgz\u201d saved [24059353/24059353]\r\n",
"\r\n"
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}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!tar -xvzf stan-src-1.1.1.tgz"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems that there is a complete copy of Boost in there, as well as a copy of Eigen."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd stan-src-1.1.1/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/snfs2/HOME/abie/new_dm/stan-src-1.1.1\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!make bin/libstan.a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O3 -o bin/stan/agrad/agrad.o src/stan/agrad/agrad.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"src/stan/agrad/agrad.hpp:2191: warning: \u2018void stan::agrad::free_memory()\u2019 defined but not used\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O3 -o bin/stan/math/matrix.o src/stan/math/matrix.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O3 -o bin/stan/agrad/matrix.o src/stan/agrad/matrix.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"src/stan/agrad/agrad.hpp:2191: warning: \u2018void stan::agrad::free_memory()\u2019 defined but not used\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ar -rs bin/libstan.a bin/stan/agrad/agrad.o bin/stan/math/matrix.o bin/stan/agrad/matrix.o\r\n",
"ar: creating bin/libstan.a\r\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!make bin/stanc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O3 -o bin/stan/command/stanc.o src/stan/command/stanc.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/statement_grammar_inst.o src/stan/gm/grammars/statement_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/whitespace_grammar_inst.o src/stan/gm/grammars/whitespace_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/expression_grammar_inst.o src/stan/gm/grammars/expression_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/var_decls_grammar_inst.o src/stan/gm/grammars/var_decls_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/statement_2_grammar_inst.o src/stan/gm/grammars/statement_2_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/term_grammar_inst.o src/stan/gm/grammars/term_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/grammars/program_grammar_inst.o src/stan/gm/grammars/program_grammar_inst.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O0 -o bin/stan/gm/ast_def.o src/stan/gm/ast_def.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ar -rs bin/libstanc.a bin/stan/gm/grammars/statement_grammar_inst.o bin/stan/gm/grammars/whitespace_grammar_inst.o bin/stan/gm/grammars/expression_grammar_inst.o bin/stan/gm/grammars/var_decls_grammar_inst.o bin/stan/gm/grammars/statement_2_grammar_inst.o bin/stan/gm/grammars/term_grammar_inst.o bin/stan/gm/grammars/program_grammar_inst.o bin/stan/gm/ast_def.o\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ar: creating bin/libstanc.a\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -lpthread -O0 -o bin/stanc bin/stan/command/stanc.o -Lbin -lstanc\r\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat src/models/basic_estimators/bernoulli.stan"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"data { \r\n",
" int<lower=0> N; \r\n",
" int<lower=0,upper=1> y[N];\r\n",
"} \r\n",
"parameters {\r\n",
" real<lower=0,upper=1> theta;\r\n",
"} \r\n",
"model {\r\n",
" theta ~ beta(1,1);\r\n",
" for (n in 1:N) \r\n",
" y[n] ~ bernoulli(theta);\r\n",
"}\r\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat src/models/basic_estimators/bernoulli.data.R"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"N <- 10\r\n",
"y <- c(0,1,0,0,0,0,0,0,0,1)"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!make src/models/basic_estimators/bernoulli"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"--- Precompiling src/stan/model/model_header.hpp for g++ ---\r\n",
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -O3 -o src/stan/model/model_header.hpp.gch src/stan/model/model_header.hpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"In file included from src/stan/prob/distributions/multivariate/discrete.hpp:5,\r\n",
" from src/stan/prob/distributions/multivariate.hpp:5,\r\n",
" from src/stan/prob/distributions.hpp:5,\r\n",
" from src/stan/model/model_header.hpp:31:\r\n",
"src/stan/agrad/agrad.hpp:2191: warning: \u2018void stan::agrad::free_memory()\u2019 defined but not used\r\n",
"\r\n",
"--- Translating Stan graphical model to C++ code ---\r\n",
"bin/stanc src/models/basic_estimators/bernoulli.stan --o=src/models/basic_estimators/bernoulli.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Model name=bernoulli_model\r\n",
"Input file=src/models/basic_estimators/bernoulli.stan\r\n",
"Output file=src/models/basic_estimators/bernoulli.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -c -O3 -o src/models/basic_estimators/bernoulli.o src/models/basic_estimators/bernoulli.cpp\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"src/stan/agrad/agrad.hpp:2191: warning: \u2018void stan::agrad::free_memory()\u2019 defined but not used\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -lpthread -O3 -o src/models/basic_estimators/bernoulli src/models/basic_estimators/bernoulli.o -Lbin -lstan\r\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd src/models/basic_estimators"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/snfs2/HOME/abie/new_dm/stan-src-1.1.1/src/models/basic_estimators"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!./bernoulli --data=bernoulli.data.R"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"STAN SAMPLING COMMAND\r\n",
"data = bernoulli.data.R\r\n",
"init = random initialization\r\n",
"init tries = 1\r\n",
"samples = samples.csv\r\n",
"append_samples = 0\r\n",
"save_warmup = 0\r\n",
"seed = 1225776884 (randomly generated)\r\n",
"chain_id = 1 (default)\r\n",
"iter = 2000\r\n",
"warmup = 1000\r\n",
"thin = 1 (default)\r\n",
"equal_step_sizes = 0\r\n",
"leapfrog_steps = -1\r\n",
"max_treedepth = 10\r\n",
"epsilon = -1\r\n",
"epsilon_pm = 0\r\n",
"delta = 0.5\r\n",
"gamma = 0.05\r\n",
"\r\n",
"Iteration: 1 / 2000 [ 0%] (Adapting)\r\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"\r\n",
"\r\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat samples.csv"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"# Samples Generated by Stan\r\n",
"#\r\n",
"# stan_version_major=1\r\n",
"# stan_version_minor=1\r\n",
"# stan_version_patch=1\r\n",
"# data=bernoulli.data.R\r\n",
"# init=random initialization\r\n",
"# append_samples=0\r\n",
"# save_warmup=0\r\n",
"# seed=1225776884\r\n",
"# chain_id=1\r\n",
"# iter=2000\r\n",
"# warmup=1000\r\n",
"# thin=1\r\n",
"# equal_step_sizes=0\r\n",
"# leapfrog_steps=-1\r\n",
"# max_treedepth=10\r\n",
"# epsilon=-1\r\n",
"# epsilon_pm=0\r\n",
"# delta=0.5\r\n",
"# gamma=0.05\r\n",
"#\r\n",
"lp__,treedepth__,stepsize__,theta\r\n",
"# (mcmc::nuts_diag) adaptation finished\r\n",
"# step size=1.43736\r\n",
"# parameter step size multipliers:\r\n",
"# 1\r\n",
"-6.85846,1,1.43736,0.194532\r\n",
"-7.96891,1,1.43736,0.469048\r\n",
"-8.72278,1,1.43736,0.0673013\r\n",
"-6.7601,1,1.43736,0.269756\r\n",
"-7.37097,1,1.43736,0.13024\r\n",
"-7.37097,1,1.43736,0.13024\r\n",
"-6.7549,1,1.43736,0.264852\r\n",
"-7.39808,1,1.43736,0.40677\r\n",
"-6.88107,1,1.43736,0.189492\r\n",
"-6.88107,1,1.43736,0.189492\r\n",
"-6.81933,1,1.43736,0.204886\r\n",
"-6.75235,1,1.43736,0.238493\r\n",
"-6.75235,1,1.43736,0.238493\r\n",
"-7.45007,1,1.43736,0.413319\r\n",
"-7.45007,2,1.43736,0.413319\r\n",
"-7.45007,1,1.43736,0.413319\r\n",
"-7.48737,1,1.43736,0.121639\r\n",
"-7.48737,1,1.43736,0.121639\r\n",
"-7.48737,1,1.43736,0.121639\r\n",
"-6.93027,1,1.43736,0.329996\r\n",
"-6.93027,1,1.43736,0.329996\r\n",
"-6.99768,1,1.43736,0.169292\r\n",
"-6.99768,1,1.43736,0.169292\r\n",
"-6.99768,1,1.43736,0.169292\r\n",
"-6.99768,1,1.43736,0.169292\r\n",
"-6.99768,1,1.43736,0.169292\r\n",
"-6.74802,1,1.43736,0.250156\r\n",
"-6.74802,1,1.43736,0.250156\r\n",
"-6.8032,1,1.43736,0.292974\r\n",
"-6.8032,1,1.43736,0.292974\r\n",
"-6.94372,1,1.43736,0.333044\r\n",
"-6.77186,1,1.43736,0.277932\r\n",
"-7.08457,1,1.43736,0.360528\r\n",
"-7.08457,1,1.43736,0.360528\r\n",
"-7.08457,1,1.43736,0.360528\r\n",
"-7.86399,1,1.43736,0.0996016\r\n",
"-7.86399,1,1.43736,0.0996016\r\n",
"-7.86399,1,1.43736,0.0996016\r\n",
"-7.02929,1,1.43736,0.350525\r\n",
"-7.02929,1,1.43736,0.350525\r\n",
"-6.83544,1,1.43736,0.200311\r\n",
"-6.83544,1,1.43736,0.200311\r\n",
"-6.95814,1,1.43736,0.17534\r\n",
"-6.74827,1,1.43736,0.25282\r\n",
"-6.74827,1,1.43736,0.25282\r\n",
"-6.74827,1,1.43736,0.25282\r\n",
"-6.74827,1,1.43736,0.25282\r\n",
"-6.74827,1,1.43736,0.25282\r\n",
"-6.7981,1,1.43736,0.290876\r\n",
"-6.75251,1,1.43736,0.261967\r\n",
"-6.75251,1,1.43736,0.261967\r\n",
"-6.75597,1,1.43736,0.265974\r\n",
"-6.75597,1,1.43736,0.265974\r\n",
"-6.75597,1,1.43736,0.265974\r\n",
"-6.74835,1,1.43736,0.253218\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-6.81502,1,1.43736,0.297502\r\n",
"-7.23866,1,1.43736,0.141577\r\n",
"-7.23866,1,1.43736,0.141577\r\n",
"-7.23866,1,1.43736,0.141577\r\n",
"-6.88085,1,1.43736,0.317788\r\n",
"-6.87183,1,1.43736,0.191487\r\n",
"-6.75117,1,1.43736,0.24017\r\n",
"-6.87961,1,1.43736,0.317458\r\n",
"-6.87961,1,1.43736,0.317458\r\n",
"-6.87961,1,1.43736,0.317458\r\n",
"-6.94214,1,1.43736,0.177985\r\n",
"-6.94214,1,1.43736,0.177985\r\n",
"-6.97485,1,1.43736,0.339746\r\n",
"-9.29138,1,1.43736,0.0532366\r\n",
"-9.29138,1,1.43736,0.0532366\r\n",
"-7.08014,1,1.43736,0.359755\r\n",
"-6.89184,1,1.43736,0.320665\r\n",
"-6.89184,1,1.43736,0.320665\r\n",
"-6.77914,1,1.43736,0.219714\r\n",
"-6.77914,1,1.43736,0.219714\r\n",
"-6.75167,1,1.43736,0.239422\r\n",
"-6.94993,1,1.43736,0.176682\r\n",
"-6.94993,1,1.43736,0.176682\r\n",
"-6.80171,1,1.43736,0.29237\r\n",
"-6.75123,1,1.43736,0.240076\r\n",
"-6.75123,1,1.43736,0.240076\r\n",
"-6.75123,1,1.43736,0.240076\r\n",
"-6.75123,1,1.43736,0.240076\r\n",
"-6.75123,1,1.43736,0.240076\r\n",
"-6.89676,1,1.43736,0.321918\r\n",
"-6.89676,1,1.43736,0.321918\r\n",
"-6.89676,1,1.43736,0.321918\r\n",
"-7.48882,1,1.43736,0.121538\r\n",
"-7.48882,1,1.43736,0.121538\r\n",
"-6.82751,1,1.43736,0.301897\r\n",
"-7.74117,1,1.43736,0.446372\r\n",
"-7.74117,1,1.43736,0.446372\r\n",
"-7.74117,1,1.43736,0.446372\r\n",
"-7.74117,1,1.43736,0.446372\r\n",
"-7.54298,1,1.43736,0.117889\r\n",
"-6.95536,1,1.43736,0.335604\r\n",
"-6.74849,1,1.43736,0.253819\r\n",
"-6.74849,1,1.43736,0.253819\r\n",
"-6.74849,1,1.43736,0.253819\r\n",
"-6.79916,1,1.43736,0.211513\r\n",
"-6.7677,1,1.43736,0.225763\r\n",
"-6.7531,1,1.43736,0.262739\r\n",
"-7.5625,1,1.43736,0.426742\r\n",
"-9.2802,1,1.43736,0.053476\r\n",
"-9.2802,1,1.43736,0.053476\r\n",
"-7.14463,1,1.43736,0.370574\r\n",
"-6.84895,1,1.43736,0.19683\r\n",
"-6.84895,1,1.43736,0.19683\r\n",
"-6.84895,1,1.43736,0.19683\r\n",
"-6.79257,1,1.43736,0.213983\r\n",
"-7.20734,1,1.43736,0.380342\r\n",
"-8.85097,1,1.43736,0.0637564\r\n",
"-7.88095,1,1.43736,0.46056\r\n",
"-6.92683,1,1.43736,0.180641\r\n",
"-14.9393,1,1.43736,0.00702256\r\n",
"-6.9366,2,1.43736,0.331442\r\n",
"-7.14224,1,1.43736,0.370189\r\n",
"-6.81225,1,1.43736,0.207078\r\n",
"-7.95167,1,1.43736,0.46741\r\n",
"-7.95167,1,1.43736,0.46741\r\n",
"-7.95167,1,1.43736,0.46741\r\n",
"-11.3568,1,1.43736,0.0244448\r\n",
"-9.18647,1,1.43736,0.563827\r\n",
"-9.18647,1,1.43736,0.563827\r\n",
"-9.18647,1,1.43736,0.563827\r\n",
"-9.58506,1,1.43736,0.0473885\r\n",
"-7.99352,1,1.43736,0.471369\r\n",
"-7.98127,1,1.43736,0.0940253\r\n",
"-7.98127,1,1.43736,0.0940253\r\n",
"-6.83694,1,1.43736,0.199912\r\n",
"-6.78464,1,1.43736,0.284797\r\n",
"-7.14638,1,1.43736,0.150818\r\n",
"-7.14638,1,1.43736,0.150818\r\n",
"-6.76283,1,1.43736,0.228913\r\n",
"-6.76283,1,1.43736,0.228913\r\n",
"-6.92614,1,1.43736,0.180763\r\n",
"-6.92614,1,1.43736,0.180763\r\n",
"-7.39296,1,1.43736,0.406112\r\n",
"-10.1862,1,1.43736,0.0376144\r\n",
"-7.61775,1,1.43736,0.433015\r\n",
"-7.61775,1,1.43736,0.433015\r\n",
"-7.61775,1,1.43736,0.433015\r\n",
"-7.61775,1,1.43736,0.433015\r\n",
"-7.17014,1,1.43736,0.374627\r\n",
"-7.17014,1,1.43736,0.374627\r\n",
"-7.43309,1,1.43736,0.125514\r\n",
"-7.21536,1,1.43736,0.14379\r\n",
"-7.0629,1,1.43736,0.160503\r\n",
"-7.0629,1,1.43736,0.160503\r\n",
"-6.76377,1,1.43736,0.228267\r\n",
"-6.76377,1,1.43736,0.228267\r\n",
"-6.76377,1,1.43736,0.228267\r\n",
"-6.76377,1,1.43736,0.228267\r\n",
"-6.76377,1,1.43736,0.228267\r\n",
"-6.76377,1,1.43736,0.228267\r\n",
"-6.8269,1,1.43736,0.202671\r\n",
"-7.73284,1,1.43736,0.445497\r\n",
"-7.73284,2,1.43736,0.445497\r\n",
"-7.73284,1,1.43736,0.445497\r\n",
"-7.73284,1,1.43736,0.445497\r\n",
"-7.73284,1,1.43736,0.445497\r\n",
"-6.7926,1,1.43736,0.2885\r\n",
"-6.7926,1,1.43736,0.2885\r\n",
"-6.7926,1,1.43736,0.2885\r\n",
"-6.7926,1,1.43736,0.2885\r\n",
"-6.7926,1,1.43736,0.2885\r\n",
"-6.7926,1,1.43736,0.2885\r\n",
"-7.09027,1,1.43736,0.157161\r\n",
"-7.09027,1,1.43736,0.157161\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.22736,1,1.43736,0.142641\r\n",
"-7.75585,1,1.43736,0.105212\r\n",
"-6.75611,1,1.43736,0.234326\r\n",
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"input": [
"import pandas as pd\n",
"df = pd.read_csv('samples.csv', skiprows=27, header=None)\n",
"df.columns = 'lp__,treedepth__,stepsize__,theta'.split(',')"
],
"language": "python",
"metadata": {},
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"input": [
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E5XnJlBx9IIe1cbiyX7OGXdMRI9TXtKUFuOACYO1a/TG0tkaPxrEhIK66ZddT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\nUARwZO/g4OBQBHBk7+Dg4FAE+P9s8kLpPdMs+AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x3494710>"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"acorr(df.theta, detrend=detrend_mean, maxlags=100);\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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ZshUrVkgeHh6Sl5eX9Nlnn5nm7969W/Ly8pI8PDyklStX2rLce4pWq5Xc3Nyk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\nIIY/EZE6O1eOAAAAB0lEQVSA/g/zCp3DM06FrAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x3533490>"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems to work, that is good."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!date"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Wed Mar 6 15:00:53 PST 2013\r\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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