adding ExamplesFromChapters

ExamplesFromChapters/Chapter1/SMS_behaviour.py

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+import pymc as mc
+
+count_data = np.loadtxt("../../Chapter1_Introduction/chp1data/txtdata.csv")
+n_count_data = len(count_data)
+
+alpha = 1.0/count_data.mean() #recall count_data is 
+                              #the variable that holds our txt counts
+
+lambda_1 = mc.Exponential( "lambda_1",  alpha )
+lambda_2 = mc.Exponential( "lambda_2", alpha )
+
+tau = mc.DiscreteUniform( "tau", lower = 0, upper = n )
+
+@mc.deterministic
+def lambda_( tau = tau, lambda_1 = lambda_1, lambda_2 = lambda_2 ):
+    out = np.zeros( n_count_data )  
+    out[:tau] = lambda_1 #lambda before tau is lambda1
+    out[tau:] = lambda_2 #lambda after tau is lambda1
+    return out
+
+observation = mc.Poisson( "obs", lambda_, value = count_data, observed = True)
+model = mc.Model( [observation, lambda_1, lambda_2, tau] )
+
+
+mcmc = mc.MCMC(model)
+mcmc.sample( 100000, 50000, 1 )

ExamplesFromChapters/Chapter2/FreqOfCheaters.py

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+import pymc as mc
+
+p = mc.Uniform( "freq_cheating", 0, 1) 
+
+@mc.deterministic
+def p_skewed( p =  p ):
+    return 0.5*p + 0.25
+
+yes_responses = mc.Binomial( "number_cheaters", 100, p_skewed, value = 35, observed = True )
+
+model = mc.Model( [yes_responses, p_skewed, p ] )
+
+### To Be Explained in Chapter 3!
+mcmc = mc.MCMC(model)
+mcmc.sample( 50000, 25000 )

ExamplesFromChapters/Chapter2/ORingFailure.py

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+import pymc as mc
+
+
+challenger_data = np.genfromtxt("../../Chapter2_MorePyMC/chp2data/challenger_data.csv", skip_header = 1, usecols=[1,2], missing_values="NA", delimiter=",")
+#drop the NA values
+challenger_data = challenger_data[ ~np.isnan(challenger_data[:,1]) ]
+
+
+temperature = challenger_data[:,0]
+D = challenger_data[:,1] #defect or not?
+
+beta = mc.Normal( "beta", 0, 0.001, value = 0 )
+alpha = mc.Normal( "alpha", 0, 0.001, value = 0 )
+
+@mc.deterministic
+def p( temp = temperature, alpha = alpha, beta = beta):
+    return 1.0/( 1. + np.exp( beta*temperature + alpha) ) 
+
+
+observed = mc.Bernoulli( "bernoulli_obs", p, value = D, observed=True)
+
+model = mc.Model( [observed, beta, alpha] )
+
+#mysterious code to be explained in Chapter 3
+map_ = mc.MAP(model)
+map_.fit()
+mcmc = mc.MCMC( model )
+mcmc.sample( 260000, 220000, 2 )

ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py

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+
+import pymc as mc
+
+
+data = np.loadtxt( "../../Chapter3_MCMC/data/mixture_data.csv",  delimiter="," )
+
+
+p = mc.Uniform( "p", 0, 1)
+
+assignment = mc.Categorical("assignment", [p, 1-p], size = data.shape[0] ) 
+
+taus = 1.0/mc.Uniform( "stds", 0, 100, size= 2)**2 #notice the size!
+centers = mc.Normal( "centers", [150, 150], [0.001, 0.001], size =2 )
+
+"""
+The below determinsitic functions map a assingment, in this case 0 or 1,
+to a set of parameters, located in the (1,2) arrays `taus` and `centers.`
+"""
+
+@mc.deterministic 
+def center_i( assignment = assignment, centers = centers ):
+        return centers[ assignment] 
+
+@mc.deterministic
+def tau_i( assignment = assignment, taus = taus ):
+        return taus[ assignment] 
+
+#and to combine it with the observations:
+observations = mc.Normal( "obs", center_i, tau_i, value = data, observed = True )
+
+#below we create a model class
+model = mc.Model( [p, assignment, taus, centers ] )
+
+
+map_ = mc.MAP( model )
+map_.fit()
+mcmc = mc.MCMC( model )
+mcmc.sample( 100000, 50000 )

ExamplesFromChapters/README.md

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+## Read Me
+
+
+Included is all PyMC examples and models *out of context*, this is for users to easily view the entire Python program. 
+