@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
    '\n        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0,                  miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0,                  tol=1e-6, solver="adamW", seed=None)\n        '
    super(FMRegressor, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.FMRegressor', self.uid)
    self._setDefault(factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW')
    kwargs = self._input_kwargs
    self.setParams(**kwargs)
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0,                  miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0,                  tol=1e-6, solver="adamW", seed=None)
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
    '\n        \n        '
    super(FMRegressor, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.FMRegressor', self.uid)
    self._setDefault(factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW')
    kwargs = self._input_kwargs
    self.setParams(**kwargs)