Merging with private repo, mostly fixed

GPy/util/misc.py

@@ -4,6 +4,16 @@
 import numpy as np
 from config import *
 
+_lim_val = np.finfo(np.float64).max
+
+_lim_val_exp = np.log(_lim_val)
+_lim_val_square = np.sqrt(_lim_val)
+_lim_val_cube = np.power(_lim_val, -3)
+
+def safe_exp(f):
+    clip_f = np.clip(f, -np.inf, _lim_val_exp)
+    return np.exp(clip_f)
+
 def chain_1(df_dg, dg_dx):
     """
     Generic chaining function for first derivative
@@ -11,6 +21,11 @@ def chain_1(df_dg, dg_dx):
     .. math::
         \\frac{d(f . g)}{dx} = \\frac{df}{dg} \\frac{dg}{dx}
     """
+    if np.all(dg_dx==1.):
+        return df_dg
+    if len(df_dg) > 1 and df_dg.shape[-1] > 1:
+        import ipdb; ipdb.set_trace()  # XXX BREAKPOINT
+        raise NotImplementedError('Not implemented for matricies yet')
     return df_dg * dg_dx
 
 def chain_2(d2f_dg2, dg_dx, df_dg, d2g_dx2):
@@ -20,7 +35,13 @@ def chain_2(d2f_dg2, dg_dx, df_dg, d2g_dx2):
     .. math::
         \\frac{d^{2}(f . g)}{dx^{2}} = \\frac{d^{2}f}{dg^{2}}(\\frac{dg}{dx})^{2} + \\frac{df}{dg}\\frac{d^{2}g}{dx^{2}}
     """
-    return d2f_dg2*(dg_dx**2) + df_dg*d2g_dx2
+    if np.all(dg_dx==1.) and np.all(d2g_dx2 == 0):
+        return d2f_dg2
+    if  len(d2f_dg2) > 1 and d2f_dg2.shape[-1] > 1:
+        raise NotImplementedError('Not implemented for matricies yet')
+    #dg_dx_2 = np.clip(dg_dx, 1e-12, _lim_val_square)**2
+    dg_dx_2 = dg_dx**2
+    return d2f_dg2*(dg_dx_2) + df_dg*d2g_dx2
 
 def chain_3(d3f_dg3, dg_dx, d2f_dg2, d2g_dx2, df_dg, d3g_dx3):
     """
@@ -29,11 +50,18 @@ def chain_3(d3f_dg3, dg_dx, d2f_dg2, d2g_dx2, df_dg, d3g_dx3):
     .. math::
         \\frac{d^{3}(f . g)}{dx^{3}} = \\frac{d^{3}f}{dg^{3}}(\\frac{dg}{dx})^{3} + 3\\frac{d^{2}f}{dg^{2}}\\frac{dg}{dx}\\frac{d^{2}g}{dx^{2}} + \\frac{df}{dg}\\frac{d^{3}g}{dx^{3}}
     """
-    return d3f_dg3*(dg_dx**3) + 3*d2f_dg2*dg_dx*d2g_dx2 + df_dg*d3g_dx3
+    if np.all(dg_dx==1.) and np.all(d2g_dx2==0) and np.all(d3g_dx3==0):
+        return d3f_dg3
+    if (  (len(d2f_dg2) > 1 and d2f_dg2.shape[-1] > 1)
+           or (len(d3f_dg3) > 1 and d3f_dg3.shape[-1] > 1)):
+        raise NotImplementedError('Not implemented for matricies yet')
+    #dg_dx_3 = np.clip(dg_dx, 1e-12, _lim_val_cube)**3
+    dg_dx_3 = dg_dx**3
+    return d3f_dg3*(dg_dx_3) + 3*d2f_dg2*dg_dx*d2g_dx2 + df_dg*d3g_dx3
 
 def opt_wrapper(m, **kwargs):
     """
-    This function just wraps the optimization procedure of a GPy
+    Thit function just wraps the optimization procedure of a GPy
     object so that optimize() pickleable (necessary for multiprocessing).
     """
     m.optimize(**kwargs)
@@ -96,3 +124,47 @@ from :class:ndarray)"""
     if len(param) == 1:
         return param[0].view(np.ndarray)
     return [x.view(np.ndarray) for x in param]
+
+def blockify_hessian(func):
+    def wrapper_func(self, *args, **kwargs):
+        # Invoke the wrapped function first
+        retval = func(self, *args, **kwargs)
+        # Now do something here with retval and/or action
+        if self.not_block_really and (retval.shape[0] != retval.shape[1]):
+            return np.diagflat(retval)
+        else:
+            return retval
+    return wrapper_func
+
+def blockify_third(func):
+    def wrapper_func(self, *args, **kwargs):
+        # Invoke the wrapped function first
+        retval = func(self, *args, **kwargs)
+        # Now do something here with retval and/or action
+        if self.not_block_really and (len(retval.shape) < 3):
+            num_data = retval.shape[0]
+            d3_block_cache = np.zeros((num_data, num_data, num_data))
+            diag_slice = range(num_data)
+            d3_block_cache[diag_slice, diag_slice, diag_slice] = np.squeeze(retval)
+            return d3_block_cache
+        else:
+            return retval
+    return wrapper_func
+
+def blockify_dhess_dtheta(func):
+    def wrapper_func(self, *args, **kwargs):
+        # Invoke the wrapped function first
+        retval = func(self, *args, **kwargs)
+        # Now do something here with retval and/or action
+        if self.not_block_really and (len(retval.shape) < 3):
+            num_data = retval.shape[0]
+            num_params = retval.shape[-1]
+            dhess_dtheta = np.zeros((num_data, num_data, num_params))
+            diag_slice = range(num_data)
+            for param_ind in range(num_params):
+                dhess_dtheta[diag_slice, diag_slice, param_ind] = np.squeeze(retval[:,param_ind])
+            return dhess_dtheta
+        else:
+            return retval
+    return wrapper_func
+