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Un-semicolon mllib.py.

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Tor Myklebust 2013-12-20 02:05:55 -05:00
parent 0a5cacb961
commit 0b494c2167
1 changed files with 11 additions and 11 deletions
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22
python/pyspark/mllib.py
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@ -143,7 +143,7 @@ def _linear_predictor_typecheck(x, coeffs):
elif (type(x) == RDD):
raise RuntimeError("Bulk predict not yet supported.")
else:
raise TypeError("Argument of type " + type(x) + " unsupported");
raise TypeError("Argument of type " + type(x) + " unsupported")
class LinearModel(object):
"""Something that has a vector of coefficients and an intercept."""
@ -170,7 +170,7 @@ def _get_unmangled_double_vector_rdd(data):
dataBytes = data.map(_serialize_double_vector)
dataBytes._bypass_serializer = True
dataBytes.cache()
return dataBytes;
return dataBytes
# If we weren't given initial weights, take a zero vector of the appropriate
# length.
@ -183,8 +183,8 @@ def _get_initial_weights(initial_weights, data):
if initial_weights.ndim != 1:
raise TypeError("At least one data element has "
+ initial_weights.ndim + " dimensions, which is not 1")
initial_weights = zeros([initial_weights.shape[0] - 1]);
return initial_weights;
initial_weights = zeros([initial_weights.shape[0] - 1])
return initial_weights
# train_func should take two parameters, namely data and initial_weights, and
# return the result of a call to the appropriate JVM stub.
@ -194,14 +194,14 @@ def _regression_train_wrapper(sc, train_func, klass, data, initial_weights):
dataBytes = _get_unmangled_double_vector_rdd(data)
ans = train_func(dataBytes, _serialize_double_vector(initial_weights))
if len(ans) != 2:
raise RuntimeError("JVM call result had unexpected length");
raise RuntimeError("JVM call result had unexpected length")
elif type(ans[0]) != bytearray:
raise RuntimeError("JVM call result had first element of type "
+ type(ans[0]) + " which is not bytearray");
+ type(ans[0]) + " which is not bytearray")
elif type(ans[1]) != float:
raise RuntimeError("JVM call result had second element of type "
+ type(ans[0]) + " which is not float");
return klass(_deserialize_double_vector(ans[0]), ans[1]);
+ type(ans[0]) + " which is not float")
return klass(_deserialize_double_vector(ans[0]), ans[1])
class LinearRegressionModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit.
@ -324,11 +324,11 @@ class KMeansModel(object):
ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd,
k, maxIterations, runs, initialization_mode)
if len(ans) != 1:
raise RuntimeError("JVM call result had unexpected length");
raise RuntimeError("JVM call result had unexpected length")
elif type(ans[0]) != bytearray:
raise RuntimeError("JVM call result had first element of type "
+ type(ans[0]) + " which is not bytearray");
return KMeansModel(_deserialize_double_matrix(ans[0]));
+ type(ans[0]) + " which is not bytearray")
return KMeansModel(_deserialize_double_matrix(ans[0]))
def _test():
import doctest