Minor updates to RKHS

benchmarks/rkhs/rkhs.py

@@ -16,81 +16,81 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-__authors__ = ["Mohsin Ali"]
-__contact__ = ["automl.org"]
-__credits__ = ["Ziyu Wang", "John Assael" and "Nando de Freitas"]
-__function__= ["RKHS"]
+__authors__  = ["Mohsin Ali"]
+__contact__  = ["automl.org"]
+__credits__  = ["Ziyu Wang", "John Assael", "Nando de Freitas"]
+__function__ = ["RKHS"]
 
 import time
 
+import numpy as np
+from scipy.spatial.distance import cdist
+
 import HPOlib.benchmarks.benchmark_util as benchmark_util
 
+
 def covSEard(hyp, x, z):
-	import numpy as np
-	from scipy.spatial.distance import cdist
-	"""
-	ARD covariance:
-		x is of dimension n X D
-		y is of dimension m X D
-	"""
-	hyp = np.exp(hyp)
+
+    """
+    ARD covariance:
+        x is of dimension n X D
+        y is of dimension m X D
+    """
+    hyp = np.exp(hyp)
 
-	D = x.shape[1]
-	X = (1 / hyp[:D]) * x
+    D = x.shape[1]
+    X = (1 / hyp[:D]) * x
 
-	Z = (1 / hyp[:D]) * z
-	K = cdist(X, Z)
+    Z = (1 / hyp[:D]) * z
+    K = cdist(X, Z)
 
-	K = hyp[D] ** 2 * np.exp(-K ** 2 / 2)
+    K = hyp[D] ** 2 * np.exp(-K ** 2 / 2)
 
-	return K
+    return K
 
 
 def rkhs_synth(params, **kwargs):
-	import numpy as np
-
-	"""
-	RKHS Function
-		Description: Synthetic heteroscedastic function generated from 2 Squared Exponential kernels
-					 for Bayesian Optimization method evaluation tasks
-		Evaluated: x \in [0,1]
-		Global Maximum: x=0.89235, f(x)=5.73839
-		Authors: Ziyu Wang, John Assael and Nando de Freitas
-	"""
-	if "x" not in params:
-		raise ValueError("No params found ['x']\n")
-	x = float(params["x"])
-	#y = float(params["y"])
+    import numpy as np
 
-	x = np.atleast_2d(x)
-	hyp_1 = np.log(np.array([0.1, 1]))
-	hyp_2 = np.log(np.array([0.01, 1]))
+    """
+    RKHS Function
+        Description: Synthetic heteroscedastic function generated from 2 Squared Exponential kernels
+                     for Bayesian Optimization method evaluation tasks
+        Evaluated: x \in [0,1]
+        Global Maximum: x=0.89235, f(x)=5.73839
+        Authors: Ziyu Wang, John Assael and Nando de Freitas
+    """
+    if "x" not in params:
+        raise ValueError("No params found ['x']\n")
+    x = float(params["x"])
+    #y = float(params["y"])
 
-	support_1 = [0.1, 0.15, 0.08, 0.3, 0.4]
-	support_2 = [0.8, 0.85, 0.9, 0.95, 0.92, 0.74, 0.91, 0.89, 0.79, 0.88, 0.86, 0.96, 0.99, 0.82]
-	vals_1 = [4, -1, 2., -2., 1.]
-	vals_2 = [3, 4, 2, 1, -1, 2, 2, 3, 3, 2., -1., -2., 4., -3.]
+    x = np.atleast_2d(x)
+    hyp_1 = np.log(np.array([0.1, 1]))
+    hyp_2 = np.log(np.array([0.01, 1]))
 
-	f = sum([vals_2[i] * covSEard(hyp_2, np.atleast_2d(np.array(s)), x) for i, s in enumerate(support_2)])
-	f += sum([vals_1[i] * covSEard(hyp_1, np.atleast_2d(np.array(s)), x) for i, s in enumerate(support_1)])
+    support_1 = [0.1, 0.15, 0.08, 0.3, 0.4]
+    support_2 = [0.8, 0.85, 0.9, 0.95, 0.92, 0.74, 0.91, 0.89, 0.79, 0.88, 0.86, 0.96, 0.99, 0.82]
+    vals_1 = [4, -1, 2., -2., 1.]
+    vals_2 = [3, 4, 2, 1, -1, 2, 2, 3, 3, 2., -1., -2., 4., -3.]
 
-	return float(f)
+    f = sum([vals_2[i] * covSEard(hyp_2, np.atleast_2d(np.array(s)), x) for i, s in enumerate(support_2)])
+    f += sum([vals_1[i] * covSEard(hyp_1, np.atleast_2d(np.array(s)), x) for i, s in enumerate(support_1)])
 
-__authors__ = ["Katharina Eggensperger", "Matthias Feurer"]
-__contact__ = "automl.org"
+    return float(f)
 
 
 def main(params, **kwargs):
-	print 'Params: ', params['x'],
-	print 'kwargs: ', kwargs,
+    print 'Params: ', params['x'],
+    print 'kwargs: ', kwargs,
 
-	y = rkhs_synth(params, **kwargs)
-	return -y
+    y = rkhs_synth(params, **kwargs)
+    return -y
 
 if __name__ == "__main__":
-	starttime = time.time()
-	args, params = benchmark_util.parse_cli()
-	result = main(params, **args)
-	duration = time.time() - starttime
-	print "Result for ParamILS: %s, %f, 1, %f, %d, %s" % \
-		("SAT", abs(duration), result, -1, str(__file__))
+    starttime = time.time()
+    args, params = benchmark_util.parse_cli()
+    result = main(params, **args)
+    duration = time.time() - starttime
+    print "Result for ParamILS: %s, %f, 1, %f, %d, %s" % \
+        ("SAT", abs(duration), result, -1, str(__file__))