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Inplace noise #533

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Merged
merged 1 commit into from
May 22, 2023
Merged

Inplace noise #533

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43 changes: 29 additions & 14 deletions src/prog_models/utils/noise_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,16 +6,22 @@
# ---------------------------
# Measurement Noise Functions
# ---------------------------
def uniform_measurement_noise(self, z : dict):
return self.OutputContainer(z.matrix + np.random.uniform(-1*self.parameters['measurement_noise'].matrix, self.parameters['measurement_noise'].matrix, size=z.matrix.shape))
def uniform_measurement_noise(self, z: dict):
noise_mat = self.parameters['measurement_noise'].matrix
z.matrix = z.matrix + np.random.uniform(-1*noise_mat, noise_mat, size=z.matrix.shape)
return z

def triangular_measurement_noise(self, z : dict):
return self.OutputContainer(z.matrix + np.random.triangular(-1*self.parameters['measurement_noise'].matrix, 0, self.parameters['measurement_noise'].matrix, size=z.matrix.shape))
def triangular_measurement_noise(self, z: dict):
noise_mat = self.parameters['measurement_noise'].matrix
z.matrix = z.matrix + np.random.triangular(-1*noise_mat, 0, noise_mat, size=z.matrix.shape)
return z

def normal_measurement_noise(self, z : dict):
return self.OutputContainer(z.matrix + np.random.normal(0, self.parameters['measurement_noise'].matrix, size=z.matrix.shape))
def normal_measurement_noise(self, z: dict):
noise_mat = self.parameters['measurement_noise'].matrix
z.matrix = z.matrix + np.random.normal(0, noise_mat, size=z.matrix.shape)
return z

def no_measurement_noise(self, z : dict) -> dict:
def no_measurement_noise(self, z: dict) -> dict:
return z

measurement_noise_functions = {
Expand All @@ -30,16 +36,25 @@ def no_measurement_noise(self, z : dict) -> dict:
# Process Noise Functions
# ---------------------------

def triangular_process_noise(self, x : dict, dt : int =1):
return self.StateContainer(x.matrix + dt*np.random.triangular(-1*self.parameters['process_noise'].matrix, 0, self.parameters['process_noise'].matrix, size=x.matrix.shape))
def triangular_process_noise(self, x: dict, dt: int = 1):
noise_mat = self.parameters['process_noise'].matrix
noise = np.random.triangular(-1*noise_mat, 0, noise_mat, size=x.matrix.shape)
x.matrix = x.matrix + dt*noise
return x

def uniform_process_noise(self, x : dict, dt : int =1):
return self.StateContainer(x.matrix + dt*np.random.uniform(-1*self.parameters['process_noise'].matrix, self.parameters['process_noise'].matrix, size=x.matrix.shape))
def uniform_process_noise(self, x: dict, dt: int = 1):
noise_mat = self.parameters['process_noise'].matrix
noise = np.random.uniform(-1*noise_mat, noise_mat, size=x.matrix.shape)
x.matrix = x.matrix + dt*noise
return x

def normal_process_noise(self, x : dict, dt : int =1):
return self.StateContainer(x.matrix + dt*np.random.normal(0, self.parameters['process_noise'].matrix, size=x.matrix.shape))
def normal_process_noise(self, x: dict, dt: int = 1):
noise_mat = self.parameters['process_noise'].matrix
noise = np.random.normal(0, noise_mat, size=x.matrix.shape)
x.matrix = x.matrix + dt*noise
return x

def no_process_noise(self, x : dict, dt :int =1) -> dict:
def no_process_noise(self, x: dict, dt: int = 1) -> dict:
return x

process_noise_functions = {
Expand Down