added conjugate gradient descent asunc

GPy/testing/cgd_tests.py

@@ -0,0 +1,56 @@
+'''
+Created on 26 Apr 2013
+
+@author: maxz
+'''
+import unittest
+import numpy
+from GPy.inference.conjugate_gradient_descent import CGD
+import pylab
+import time
+from scipy.optimize.optimize import rosen, rosen_der
+
+
+class Test(unittest.TestCase):
+
+    def testMinimizeSquare(self):
+        f = lambda x: x ** 2 + 2 * x - 2
+
+if __name__ == "__main__":
+    # import sys;sys.argv = ['', 'Test.testMinimizeSquare']
+#     unittest.main()
+    N = 2
+    A = numpy.random.rand(N) * numpy.eye(N)
+    b = numpy.random.rand(N)
+#     f = lambda x: numpy.dot(x.T.dot(A), x) + numpy.dot(x.T, b)
+#     df = lambda x: numpy.dot(A, x) - b
+
+    f = rosen
+    df = rosen_der
+    x0 = numpy.random.randn(N) * .5
+
+    opt = CGD()
+
+    fig = pylab.figure("cgd optimize")
+    if fig.axes:
+        ax = fig.axes[0]
+        ax.cla()
+    else:
+        ax = fig.add_subplot(111, projection='3d')
+
+    interpolation = 40
+    x, y = numpy.linspace(-1, 1, interpolation)[:, None], numpy.linspace(-1, 1, interpolation)[:, None]
+    X, Y = numpy.meshgrid(x, y)
+    fXY = numpy.array([f(numpy.array([x, y])) for x, y in zip(X.flatten(), Y.flatten())]).reshape(interpolation, interpolation)
+
+    ax.plot_wireframe(X, Y, fXY)
+    xopts = [x0.copy()]
+    optplts, = ax.plot3D([x0[0]], [x0[1]], zs=f(x0), marker='o', color='r')
+
+    def callback(x, *a, **kw):
+        xopts.append(x.copy())
+        time.sleep(.3)
+        optplts._verts3d = [numpy.array(xopts)[:, 0], numpy.array(xopts)[:, 1], [f(xs) for xs in xopts]]
+        fig.canvas.draw()
+
+    res = opt.fmin(f, df, x0, callback, messages=True, report_every=1)