Fixed code bugs, and some typos in the slides.

R/inhomPPLik.R

@@ -52,9 +52,10 @@ Kernel = function(c_matrix) {
 #' @param B -- the full transmission matrix
 #' @return scalar product part of the epidemic likelihoood
 prod_part = function(t_inf_j, events, B) {
-	waifw = sapply(t_inf_j, function(t) events[,1] < t & events[,2])
-	lambdaj = colSums(B * waifw)
-	I0 = which.min(t_inf_j)
+	is_infected = t_inf_j < Inf
+	waifw = sapply(t_inf_j, function(t) events[,1] < t & t < events[,2])
+	lambdaj = colSums(B[,is_infected] * waifw[,is_infected])
+	I0 = which.min(t_inf_j[is_infected])
 	print(waifw)
 	sum(log(lambdaj[-I0]))
 }
@@ -79,7 +80,7 @@ interval_intersect = function(interval_i, interval_j) {
 #' @param B -- the full transmission matrix
 #' @return -- scalar integrated infection pressure for the epidemic
 integral_part = function(t_inf_j, events, B) {
-	i_infected = events[,1] < Inf # Infection time finite
+	i_infected = events[,1] < Inf 
 	E = interval_intersect(events[i_infected,], cbind(0, t_inf_j))
 	integral = E * B[i_infected,]
 	sum(integral)

epiLikelihoods.tex

@@ -404,7 +404,7 @@ $$
 
 \begin{itemize}
 \item We can now calculate $\lambda_i(t)$ for each member $i$ of $\mathcal{S}$:
-$$\lambda_j(t) = \beta \sum{i \in \mathcal{I}(t)} c_{ij}$$
+$$\lambda_j(t) = \beta \sum_{i \in \mathcal{I}(t)} c_{ij}$$
 \item Likelihood takes a product over \emph{all} individuals in the population $\mathcal{P}$:
 \begin{eqnarray*}
 L(\bm{t}^{SI} | \beta) & = & \prod_{j \in \mathcal{P}}  \left[ \lambda_j(s^-) e^{-\int_{I_0}^{s} \lambda_j(t) \mathrm{d}t} \right]^{t^{SI}_j < T_{max}} \left[ e^{-\int_{I_0}^{s} \lambda_j(t) \mathrm{d}t} \right]^{t^{SI}_j \geq T_{max}} \\
@@ -516,25 +516,23 @@ $$W:\;w_{ij} = t^{inf}_i < t^{inf}_j < t^{rem}_i$$
 \begin{lstlisting}[language=R]
 # R
 prod_part = function(t_inf_j, events, B) {
-    waifw = sapply(t_inf_j, function(t) events[,1] < t & events[,2])
-    lambdaj = colSums(B * waifw)
-    I0 = which.min(t_inf_j)
+    is_infected = t_inf_j < Inf
+    waifw = sapply(t_inf_j, function(t) events[,1] < t & t < events[,2])
+    lambdaj = colSums(B[,is_infected] * waifw[,is_infected])
+    I0 = which.min(t_inf_j[is_infected])
     sum(log(lambdaj[-I0]))
-}
- \end{lstlisting}
+} \end{lstlisting}
 
 
  \begin{lstlisting}[language=Python]
  # Python
  import numpy as np
- def prod_part(t_inf_j, events, B):
-    infec, remove = events[:,0], events[:,1]
-    waifw = (infec[:,None] < t_inf_j[None,:]) & (t_inf_j[None,:] < remove[:,None])
-    lambdaj = np.sum(waifw * B, axis=0)
-    I0 = np.argmin(t_inf_j)
-    return np.sum(np.log(np.delete(lambdaj,I0)))
+    is_infected = t_inf_j < np.inf
+    waifw = (events[0][:, None] < t_inf_j[None, :]) & (t_inf_j[None, :] < events[1][:, None])
+    lambdaj = np.sum(np.multiply(B[:, is_infected], waifw[:, is_infected]), axis=0)
+    I0 = np.argmin(t_inf_j[is_infected])
+    return np.sum(np.log(np.delete(lambdaj, I0)))
 \end{lstlisting}
-
 \end{frame}
 
 
@@ -594,18 +592,18 @@ All we have to do to calculate the integral is multiply relevant rows of $B$ and
 \begin{lstlisting}[language=R]
 # R
 integral_part = function(t_inf_j, events, B) {
-	i_infected = events[,1] < Inf # Infection time finite
-	E = interval_intersect(events[i_infected,], cbind(0, t_inf_j))
-	integral = E * B[i_infected,]
-	sum(integral)
+    i_infected = events[,1] < Inf
+    E = interval_intersect(events[i_infected,], cbind(0, t_inf_j))
+    integral = E * B[i_infected,]
+    sum(integral)
 }
 \end{lstlisting}
 
 \begin{lstlisting}[language=Python]
 # Python
 def integral_part(t_inf_j, events, B):
-    i_infected = events[:,1] < np.inf
-    E = interval_intersect((events[:,0],events[:,1]), (0., t_inf_j))
+    i_infected = events[0] < np.inf
+    E = interval_intersect((events[0][i_infected],events[1][i_infected]), (np.zeros_like(t_inf_j), t_inf_j))
     integral = E * B[i_infected,:]
     return np.sum(integral)
 \end{lstlisting}
@@ -653,8 +651,8 @@ def log_likelihood(par, t_inf, t_rem, kernel):
 \vspace{12pt}
 \item Measure your code in terms of \textcolor{blue}{lines not written}
   \begin{itemize}
-  \item 28 lines of R
-  \item x lines of Python
+  \item 29 lines of R
+  \item 25 lines of Python
   \end{itemize}
 \end{itemize}
 

python/inhomPPLik.py

@@ -41,10 +41,10 @@ def prod_part(t_inf_j, events, B):
     :param B: the full transmission matrix
     :return: scalar product part of the epidemic likelihoood
     """
+    is_infected = t_inf_j < np.inf
     waifw = (events[0][:, None] < t_inf_j[None, :]) & (t_inf_j[None, :] < events[1][:, None])
-    lambdaj = np.sum(waifw * B, axis=0)
-    I0 = np.argmin(t_inf_j)
-    print(waifw)
+    lambdaj = np.sum(np.multiply(B[:, is_infected], waifw[:, is_infected]), axis=0)
+    I0 = np.argmin(t_inf_j[is_infected])
     return np.sum(np.log(np.delete(lambdaj, I0)))
 
 
@@ -69,8 +69,8 @@ def integral_part(t_inf_j, events, B):
     :return: the integrated infection pressure for the SI epidemic"""
 
     i_infected = events[0] < np.inf
-    E = interval_intersect((events[0][i_infected],events[1][i_infected]), (np.zeros_like(t_inf_j), t_inf_j))
-    integral = np.multiply(E, B[i_infected,:])
+    E = interval_intersect((events[0][i_infected], events[1][i_infected]), (np.zeros_like(t_inf_j), t_inf_j))
+    integral = np.multiply(E, B[i_infected, :])
     return np.sum(integral)
 
 
@@ -85,9 +85,7 @@ def log_likelihood(par, t_inf, t_rem, kernel):
     :"""
     B = kernel(par)
     prod = prod_part(t_inf, (t_inf, t_rem), B)
-    print("prod = ", prod)
     integral = integral_part(t_inf, (t_inf, t_rem), B)
-    print('integral = ', integral)
     return prod - integral