#!/usr/bin/env python # -*- coding: utf-8 -*- from numpy import * from matplotlib import pylab import stochbb X1 = stochbb.gamma(3, 100) + 100 X2 = stochbb.gamma(3, 120) + 100 Y1 = stochbb.gamma(3, 140) + 100 Y2 = stochbb.gamma(3, 130) + 100 Z1 = stochbb.condchain(X1, X2, Y1, Y2) Tmin, Tmax, steps = 0, 2000., 100 t = linspace(Tmin, Tmax, steps) pX1 = zeros(steps, ) X1.density().eval(Tmin, Tmax, pX1) pX2 = zeros(steps, ) X2.density().eval(Tmin, Tmax, pX2) pY1 = zeros(steps, ) Y1.density().eval(Tmin, Tmax, pY1) pY2 = zeros(steps, ) Y2.density().eval(Tmin, Tmax, pY2) pZ1 = zeros(steps, ) Z1.density().eval(Tmin, Tmax, pZ1) pylab.plot(t, pX1, label="X1") pylab.plot(t, pX2, label="X2") pylab.plot(t, pY1, label="Y1") pylab.plot(t, pY2, label="Y2")
# # This example uses 3 Gamma distributions (of wich one is a shifted Gamma distribution) and the # minimum(,) operator. For both conditions (control, experimental) there is a common stage C # following some Gamma distribution. Under control condition, the common stage is followed by a # second Gamma distributed stage X1 (C -> X1). Under the experimental condition, the common stage # also triggers a second stage X2 with a shifted Gamma distribution parallel with the stage X1 # present under the control conditon. This stage is delayed by 300ms relative to the parallel stage # X1, but completes much faster than X1 once it started. The response latency under the exp. # condition is then the chain C -> max(X1, X2). # # # Processing model # d = 300 C = stochbb.gamma(3,20) X1 = stochbb.gamma(10,30) # shifted gamma X2 = stochbb.gamma(1,70) + d # response latency control condition # is simply R = C + X1 Rc = C + X1 # and for the experimental condition Re = C + stochbb.minimum(X1, X2) Tmin, Tmax, N = 0, 1200, 1200; t = linspace(Tmin, Tmax, N); Tc = empty((N,)); Rc.density().eval(Tmin, Tmax, Tc) Te = empty((N,)); Re.density().eval(Tmin, Tmax, Te)
from numpy import * import stochbb from matplotlib import pylab Tmin, Tmax, N = 0, 700, 1000; t = linspace(Tmin, Tmax, N) stochbb.Logger.addHandler(stochbb.IOLogHandler()) X = stochbb.gamma(10,10); Y = stochbb.gamma(10,20); Z = X+Y+100; print X.density().rangeEst(0.01) print Y.density().rangeEst(0.01) print Z.density().rangeEst(0.01) dX = empty(N,); X.density().eval(Tmin, Tmax, dX); dY = empty(N,); Y.density().eval(Tmin, Tmax, dY); dZ = empty(N,); Z.density().eval(Tmin, Tmax, dZ); pylab.plot(t, dX) pylab.plot(t, dY) pylab.plot(t, dZ) pylab.show()
#!/usr/bin/env python # -*- coding: utf-8 -*- from numpy import * from matplotlib import pylab import stochbb X1 = stochbb.gamma(3, 100) + 100; X2 = stochbb.gamma(3, 120) + 100; Y1 = stochbb.gamma(3, 140) + 100; Y2 = stochbb.gamma(3, 130) + 100; Z1 = stochbb.condchain(X1, X2, Y1, Y2) Tmin, Tmax, steps = 0, 2000., 100 t = linspace(Tmin, Tmax, steps) pX1 = zeros(steps,); X1.density().eval(Tmin, Tmax, pX1); pX2 = zeros(steps,); X2.density().eval(Tmin, Tmax, pX2); pY1 = zeros(steps,); Y1.density().eval(Tmin, Tmax, pY1); pY2 = zeros(steps,); Y2.density().eval(Tmin, Tmax, pY2); pZ1 = zeros(steps,); Z1.density().eval(Tmin, Tmax, pZ1); pylab.plot(t, pX1, label="X1") pylab.plot(t, pX2, label="X2") pylab.plot(t, pY1, label="Y1") pylab.plot(t, pY2, label="Y2") pylab.plot(t, pZ1, label="Z1") pylab.legend() pylab.show()
# # This example uses 3 Gamma distributions (of wich one is a shifted Gamma distribution) and the # minimum(,) operator. For both conditions (control, experimental) there is a common stage C # following some Gamma distribution. Under control condition, the common stage is followed by a # second Gamma distributed stage X1 (C -> X1). Under the experimental condition, the common stage # also triggers a second stage X2 with a shifted Gamma distribution parallel with the stage X1 # present under the control conditon. This stage is delayed by 300ms relative to the parallel stage # X1, but completes much faster than X1 once it started. The response latency under the exp. # condition is then the chain C -> max(X1, X2). # # # Processing model # d = 300 C = stochbb.gamma(3, 20) X1 = stochbb.gamma(10, 30) # shifted gamma X2 = stochbb.gamma(1, 70) + d # response latency control condition # is simply R = C + X1 Rc = C + X1 # and for the experimental condition Re = C + stochbb.minimum(X1, X2) Tmin, Tmax, N = 0, 1200, 1200 t = linspace(Tmin, Tmax, N) Tc = empty((N, )) Rc.density().eval(Tmin, Tmax, Tc) Te = empty((N, ))
# # Log word frequency (per mililon) # is uniform distr. on the interval [0,4] f = stochbb.uniform(0, 4) # Predictability is unif. distributed on the interval [0,1] # somewhat unrealistic but this is just an example p = stochbb.uniform(0, 1) # # Cognitive model # # "lexical" stage is gamma distributed with k=5*f+5, theta=10 L = stochbb.gamma(5 * f + 5, 10) # "semantic" stage is gamma distributed with k=10*p+5, theta=20 S = stochbb.gamma(10 * p + 5, 20) # motor stage, gamma distributed with k=10, theta=30 M = stochbb.gamma(10, 30) # response latency is simply R = L + S + M R = L + S + M Tmin, Tmax, N = 0, 1200, 1000 t = linspace(Tmin, Tmax, N) dt = float(Tmax - Tmin) / N pL = empty(N, ) L.density().eval(Tmin, Tmax, pL) pS = empty(N, ) S.density().eval(Tmin, Tmax, pS)
from numpy import * import stochbb from matplotlib import pylab X1 = stochbb.gamma(2,1.1)+1 X2 = stochbb.gamma(2,1)+1 X3 = X1 + X2 Tmin, Tmax, N = 0, 4, 100 t = linspace(Tmin, Tmax, N) dX1 = empty((N,)); X1.density().eval(Tmin, Tmax, dX1) dX2 = empty((N,)); X2.density().eval(Tmin, Tmax, dX2) dX3 = empty((N,)); X3.density().eval(Tmin, Tmax, dX3) pylab.plot(t, dX1, label="X1") pylab.plot(t, dX2, label="X2") pylab.plot(t, dX3, label="X1+X2") Tmin, Tmax, N = 0, 12, 300 t = linspace(Tmin, Tmax, N) dX12 = empty((N,)); X1.density().eval(Tmin, Tmax, dX12) dX22 = empty((N,)); X2.density().eval(Tmin, Tmax, dX22) dX32 = empty((N,)); X3.density().eval(Tmin, Tmax, dX32) pylab.plot(t, dX12, "--", label="X1") pylab.plot(t, dX22, "--", label="X2") pylab.plot(t, dX32, "--", label="X1+X2") pylab.legend() pylab.show()
from numpy import * import stochbb from matplotlib import pylab Tmin, Tmax, N = 0, 700, 1000 t = linspace(Tmin, Tmax, N) stochbb.Logger.addHandler(stochbb.IOLogHandler()) X = stochbb.gamma(10, 10) Y = stochbb.gamma(10, 20) Z = X + Y + 100 print X.density().rangeEst(0.01) print Y.density().rangeEst(0.01) print Z.density().rangeEst(0.01) dX = empty(N, ) X.density().eval(Tmin, Tmax, dX) dY = empty(N, ) Y.density().eval(Tmin, Tmax, dY) dZ = empty(N, ) Z.density().eval(Tmin, Tmax, dZ) pylab.plot(t, dX) pylab.plot(t, dY) pylab.plot(t, dZ) pylab.show()
#
# Log word frequency (per mililon)
# is uniform distr. on the interval [0,4]
f = stochbb.uniform(0,4)
# Predictability is unif. distributed on the interval [0,1]
# somewhat unrealistic but this is just an example
p = stochbb.uniform(0,1)
#
# Cognitive model
#
# "lexical" stage is gamma distributed with k=5*f+5, theta=10
L = stochbb.gamma(5*f+5, 10);
# "semantic" stage is gamma distributed with k=10*p+5, theta=20
S = stochbb.gamma(10*p+5, 20);
# motor stage, gamma distributed with k=10, theta=30
M = stochbb.gamma(10, 30)
# response latency is simply R = L + S + M
R = L+S+M
Tmin, Tmax, N = 0, 1200, 1000;
t = linspace(Tmin, Tmax, N); dt = float(Tmax-Tmin)/N
pL = empty(N,); L.density().eval(Tmin, Tmax, pL)
pS = empty(N,); S.density().eval(Tmin, Tmax, pS)
pM = empty(N,); M.density().eval(Tmin, Tmax, pM)
pR = empty(N,); cProfile.run("R.density().eval(Tmin, Tmax, pR)")
from numpy import * import stochbb from matplotlib import pylab X1 = stochbb.gamma(2, 1.1) + 1 X2 = stochbb.gamma(2, 1) + 1 X3 = X1 + X2 Tmin, Tmax, N = 0, 4, 100 t = linspace(Tmin, Tmax, N) dX1 = empty((N, )) X1.density().eval(Tmin, Tmax, dX1) dX2 = empty((N, )) X2.density().eval(Tmin, Tmax, dX2) dX3 = empty((N, )) X3.density().eval(Tmin, Tmax, dX3) pylab.plot(t, dX1, label="X1") pylab.plot(t, dX2, label="X2") pylab.plot(t, dX3, label="X1+X2") Tmin, Tmax, N = 0, 12, 300 t = linspace(Tmin, Tmax, N) dX12 = empty((N, )) X1.density().eval(Tmin, Tmax, dX12) dX22 = empty((N, )) X2.density().eval(Tmin, Tmax, dX22) dX32 = empty((N, )) X3.density().eval(Tmin, Tmax, dX32) pylab.plot(t, dX12, "--", label="X1")
