Initial weights in Scala are ones; do that too. Also fix some errors.

python/pyspark/mllib/_common.py

@@ -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):