models.py
def repredict(dv, t1, t2):
  """
  Takes decision values and returns predictions, given thresholds 
  for the overall level and for distance to the row-wise maximum.
  
  Params:
    dv - 2d array of decision values
    t1 - either a scalar threshold, or a vector of length(dv.shape[1])
         all dvs > t1 are positive
    t2 - all dvs >= row_max - t2 are positive
    
  Returns:
    predictions (0-1) from these dvs with the given threshold.
  """
  pred = ((dv - t1) > 0).astype(float)
  max_dv = dv.max(1)
  for k in range(pred.shape[0]):
    cut = max_dv[k] - t2
    idx = (dv[k, :] >= cut) 
    pred[k, idx] = 1
  return pred

  Takes decision values and returns predictions, given thresholds 
  for the overall level and for distance to the row-wise maximum.
  
  Params:
    dv - 2d array of decision values
    t1 - either a scalar threshold, or a vector of length(dv.shape[1])
         all dvs > t1 are positive
    t2 - all dvs >= row_max - t2 are positive
    
  Returns:
    predictions (0-1) from these dvs with the given threshold.
  
Takes decision values and returns predictions, given thresholds
for the overall level and for distance to the row-wise maximum.

2d array of decision values
t1 - either a scalar threshold, or a vector of length(dv.shape[1])
all dvs > t1 are positive
t2 - all dvs >= row_max - t2 are positive

predictions (0-1) from these dvs with the given threshold.
Takes decision values and returns predictions given thresholds for the overall level and for distance to the row - wise maximum . 2d array of decision values t1 - either a scalar threshold or a vector of length ( dv . shape [ 1 ] ) all dvs > t1 are positive t2 - all dvs > = row_max - t2 are positive predictions ( 0 - 1 ) from these dvs with the given threshold .
def repredict(dv, t1, t2):
  pred = ((dv - t1) > 0).astype(float)
  max_dv = dv.max(1)
  for k in range(pred.shape[0]):
    cut = max_dv[k] - t2
    idx = (dv[k, :] >= cut) 
    pred[k, idx] = 1
  return pred
def repredict ( dv , t1 , t2 ) : pred = ( ( dv - t1 ) > 0 ) . astype ( float ) max_dv = dv . max ( 1 ) for k in range ( pred . shape [ 0 ] ) : cut = max_dv [ k ] - t2 idx = ( dv [ k , : ] >= cut ) pred [ k , idx ] = 1 return pred
Takes decision values and returns predictions, given thresholds
for the overall level and for distance to the row-wise maximum.
Takes decision values and returns predictions given thresholds for the overall level and for distance to the row - wise maximum .
"""
  Takes decision values and returns predictions, given thresholds 
  for the overall level and for distance to the row-wise maximum.
  
  Params:
    dv - 2d array of decision values
    t1 - either a scalar threshold, or a vector of length(dv.shape[1])
         all dvs > t1 are positive
    t2 - all dvs >= row_max - t2 are positive
    
  Returns:
    predictions (0-1) from these dvs with the given threshold.
  """
dv
t1
t2