update on machine learning

doc/pub/How2ReadData/html/src/ecoli_inverse.py

@@ -0,0 +1,27 @@
+import numpy as np
+
+data = np.loadtxt('ecoli.csv', delimiter=',')
+t_experiment = data[:,0]
+N_experiment = data[:,1]
+
+def error(p):
+    r = p[0]
+    T = 1200     # cell can divide after T sec
+    t_max = 5*T  # 5 generations in experiment
+    t = np.linspace(0, t_max, len(t_experiment))
+    dt = (t[1] - t[0])
+    N = np.zeros(t.size)
+
+    N[0] = 100
+    for n in range(0, len(t)-1, 1):
+        N[n+1] = N[n] + r*dt*N[n]
+
+    e = np.sqrt(np.sum((N - N_experiment)**2))/N[0]  # error measure
+    e = abs(N[-1] - N_experiment[-1])/N[0]
+    print 'r=', r, 'e=',e
+    return e
+
+from scipy.optimize import minimize
+
+p = minimize(error, [0.0006], tol=1E-5)
+print p