ODIn/spark-instrumented-optimizer
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Initial weights in Scala are ones; do that too. Also fix some errors.

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Tor Myklebust 2013-12-25 00:53:48 -05:00
parent 4e821390bc
commit 02208a175c
1 changed files with 6 additions and 6 deletions
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12
python/pyspark/mllib/_common.py
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@ -15,7 +15,7 @@
# limitations under the License.
#
from numpy import ndarray, copyto, float64, int64, int32, zeros, array_equal, array, dot, shape
from numpy import ndarray, copyto, float64, int64, int32, ones, array_equal, array, dot, shape
from pyspark import SparkContext
# Double vector format:
@ -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).__name__ + " unsupported")
def _get_unmangled_rdd(data, serializer):
dataBytes = data.map(serializer)
@ -182,11 +182,11 @@ def _get_initial_weights(initial_weights, data):
initial_weights = data.first()
if type(initial_weights) != ndarray:
raise TypeError("At least one data element has type "
+ type(initial_weights) + " which is not ndarray")
+ type(initial_weights).__name__ + " which is not ndarray")
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])
initial_weights = ones([initial_weights.shape[0] - 1])
return initial_weights
# train_func should take two parameters, namely data and initial_weights, and
@ -200,10 +200,10 @@ def _regression_train_wrapper(sc, train_func, klass, data, initial_weights):
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]).__name__ + " 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")
+ type(ans[0]).__name__ + " which is not float")
return klass(_deserialize_double_vector(ans[0]), ans[1])
def _serialize_rating(r):