def svm_epoch(model_table, dat_table, label, arr, epoch, step=0.1, mu=0.1):
''' Creates a query to update the SVM model
'''
return iutil.bismarck_epoch(
model_table, dat_table, 'svm(__PREV_MODEL__, %(arr)s, '
'%(label)s, %(step)s, %(mu)s)' % {
'arr': arr,
'label': label,
'step': step,
'mu': mu
}, epoch, label)
def logr_epoch(model_table, dat_table, label, arr, epoch, step=0.1, mu=0.1):
''' Generates a query to train the model
This will create a query to insert into the state table.
@param model_table: name of table to store models
@param dat_table: name of table we iterate over
@param label: name of label column
@param arr: UDF that returns an double array stored as a string
@param epoch: the epoch number (first epoch is 1, the 0th epoch is an empty string)
'''
return iutil.bismarck_epoch(model_table, dat_table, 'logr(__PREV_MODEL__, %(arr)s, '
'%(label)s, %(step)s, %(mu)s)' % {'arr':arr, 'label':label, 'step':step,
'mu':mu}, epoch, label)
def logr_epoch(model_table, dat_table, label, arr, epoch, step=0.1, mu=0.1):
''' Generates a query to train the model
This will create a query to insert into the state table.
@param model_table: name of table to store models
@param dat_table: name of table we iterate over
@param label: name of label column
@param arr: UDF that returns an double array stored as a string
@param epoch: the epoch number (first epoch is 1, the 0th epoch is an empty string)
'''
return iutil.bismarck_epoch(model_table, dat_table, 'logr(__PREV_MODEL__, %(arr)s, '
'%(label)s, %(step)s, %(mu)s)' % {'arr':arr, 'label':label, 'step':step,
'mu':mu}, epoch, label)
def svm_epoch(model_table, dat_table, label, arr, epoch, step=0.1, mu=0.1):
''' Creates a query to update the SVM model
'''
return iutil.bismarck_epoch(model_table, dat_table, 'svm(__PREV_MODEL__, %(arr)s, '
'%(label)s, %(step)s, %(mu)s)' % {'arr':arr, 'label':label, 'step':step,
'mu':mu}, epoch, label)
