def __init__(self):
self.rls = DoubleRLSFilter(40)
self.rls.input_sampling_rate = fs
self.rls.memory = 0.999
self.rls.w = np.ones((40, ))
self.rls.P = np.eye(40) / 40
self.rls.learning = False
def __init__(self): self.rls = DoubleRLSFilter(40) self.rls.input_sampling_rate = fs self.rls.memory = 0.999 self.rls.w = np.ones((40,)) self.rls.P = np.eye(40)/40 self.rls.learning = False
class Filter:
def __init__(self):
self.rls = DoubleRLSFilter(40)
self.rls.input_sampling_rate = fs
self.rls.memory = 0.999
self.rls.w = np.ones((40,))
self.rls.P = np.eye(40)/40
self.rls.learning = False
def process(self, input):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = fs
self.rls.set_input_port(0, infilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = fs
outfilter.set_input_port(0, self.rls, 0)
outfilter.process(input.shape[1])
return output
class Filter:
def __init__(self):
self.rls = DoubleRLSFilter(40)
self.rls.input_sampling_rate = fs
self.rls.memory = 0.999
self.rls.w = np.ones((40, ))
self.rls.P = np.eye(40) / 40
self.rls.learning = False
def process(self, input):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = fs
self.rls.set_input_port(0, infilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = fs
outfilter.set_input_port(0, self.rls, 0)
outfilter.process(input.shape[1])
return output
def filter(input):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = 48000
rls = DoubleRLSFilter(10)
rls.input_sampling_rate = 48000
rls.memory = 0.999
rls.learning = True
rls.set_input_port(0, infilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = 48000
outfilter.set_input_port(0, rls, 0)
outfilter.process(1000)
rls.learning = False
outfilter.process(input.shape[1] - 1000)
return output
def filter(input): import numpy as np output = np.zeros(input.shape, dtype=np.float64) infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = 48000 rls = DoubleRLSFilter(10) rls.input_sampling_rate = 48000 rls.memory = 0.99 rls.learning = True rls.set_input_port(0, infilter, 0) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = 48000 outfilter.set_input_port(0, rls, 0) outfilter.process(1000) rls.learning = False outfilter.process(input.shape[1] - 1000) return output
def __init__(self): self.rls = DoubleRLSFilter(5) self.rls.input_sampling_rate = fs self.rls.memory = 0.999
def __init__(self):
self.rls = DoubleRLSFilter(5)
self.rls.input_sampling_rate = fs
self.rls.memory = 0.999
def RLS_bad_P_size_test(): import numpy as np rls = DoubleRLSFilter(100) rls.P = np.ones((10,10))
def RLS_bad_w_size_test(): import numpy as np rls = DoubleRLSFilter(100) rls.w = np.ones((10,))
def RLS_bad_P_dim_test(): import numpy as np rls = DoubleRLSFilter(100) rls.P = np.array((100,))
def RLS_bad_w_dim_test(): import numpy as np rls = DoubleRLSFilter(100) rls.w = np.array(())
def RLS_bad_P_size_test():
import numpy as np
rls = DoubleRLSFilter(100)
rls.P = np.ones((10, 10))
def RLS_bad_P_dim_test():
import numpy as np
rls = DoubleRLSFilter(100)
rls.P = np.array((100, ))
def RLS_bad_w_size_test():
import numpy as np
rls = DoubleRLSFilter(100)
rls.w = np.ones((10, ))
def RLS_bad_w_dim_test():
import numpy as np
rls = DoubleRLSFilter(100)
rls.w = np.array(())