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diff --git a/src/generalized_unsented_transform/evaluate_from_sigma_points.py b/src/generalized_unsented_transform/evaluate_from_sigma_points.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a82ac3a4b28419884e10f2d33c5aa81aaaa4cef
--- /dev/null
+++ b/src/generalized_unsented_transform/evaluate_from_sigma_points.py
@@ -0,0 +1,38 @@
+from numpy.linalg import matrix_power
+import numpy as np
+
+
+
+'''
+ This function is a tool that is used to evaluate the sample statistics
+ of sigma points of an unscented transform
+
+ INPUTS
+
+ sigma_points - Matrix of sigma points
+ weights - Vector of weights corresponding to each sigma point
+
+ OUTPUTS
+
+ sigma_mean - Sample mean of sigma points
+ sigma_cov - Sample covariance matrix of sigma points
+ sigma_skew - Sample diagonal component of skewness tensor
+ sigma_kurt - Sample diagonal component of kurtosis tensor
+'''
+
+def Evaluate_sample_statistics(sigma_points, weights):
+
+ n = sigma_points.shape[0]
+ # row_weights = []
+ # weights = weights.reshape(1, -1) # Convert to row vector (need testing)
+ # Mean
+ sigma_mean = np.sum(np.matmul(sigma_points, np.tile(weights,(n, 1))))
+ # Covariance
+ Z = (sigma_points - sigma_mean)
+ # print(np.diag(np.transpose(weights)[:,:]))
+ sigma_cov = np.sum(Z * np.diag(np.array(np.transpose(weights))[0])* np.transpose(Z))
+ # Diagonal skewness
+ sigma_skew = np.sum(np.matmul(np.power(Z, 3), np.tile(weights,(n, 1))))
+ # Diagonal kurtosis
+ sigma_kurt = np.sum(np.matmul(np.power(Z, 4), np.tile(weights,(n, 1))))
+ return sigma_mean, sigma_cov, sigma_skew, sigma_kurt
diff --git a/src/generalized_unsented_transform/generalized_unscented_transform.py b/src/generalized_unsented_transform/generalized_unscented_transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..3800af8919943d7e97b57bd2e4ae758e7938494f
--- /dev/null
+++ b/src/generalized_unsented_transform/generalized_unscented_transform.py
@@ -0,0 +1,134 @@
+from concurrent.futures.process import _MAX_WINDOWS_WORKERS
+import numpy as np
+from scipy.linalg import sqrtm
+from numpy.linalg import matrix_power, solve
+import warnings
+
+
+'''
+mu - Mean of random vector
+P - Covariance matrix
+x_skew - Vector of diagonal components of the skewness tensor
+x_kurt - Vector of diagonal components of the kurtosis tensor
+lb - Vector of lower bound of the state
+ub - Vector of upper bound of the state
+
+OUTPUTS
+
+x - Matrix of sigma points
+weights - Vector of weights corresponding to each sigma point
+s - Vector of proportionality constants used to generate
+ the sigma points
+'''
+def generalized_ut(mu, P, x_skew=None, x_kurt=None, lb=None, ub=None):
+
+ # Get the number of states
+ n = len(mu)
+ # Evaluate the matrix square root via singular value decomposition
+ # U1, S, Vh = np.linalg.svd(P)
+ # C = U1 * np.diag(np.sqrt(np.diag(S))) * U1
+ C = sqrtm(P)
+ # Handle the arguments for skewness and kurtosis
+ if x_skew == None: # If no diagonal component of skewness is specified
+ warnings.warn('No skewness specified: Gaussian skewness and kurtosis is assumed')
+ x_skew = 0*mu # Assume gaussian skewness if not provided
+ x_kurt = 3*np.ones(n) # Assume gaussian diagonal kurtosis in turn
+
+ if x_kurt == None: # If no diagonal component of kurtosis is specified
+ warnings.warn('No kurtosis specified: kurtosis is selected to satisfy skewness kurtosis relationship')
+ # Manually ensure kurtosis skew relationship is satisfied
+ x_kurt = matrix_power(C, 4) * matrix_power(solve((matrix_power(C, 3)), x_skew), 2)
+ x_kurt = 1.1 * x_kurt
+
+ # Handle when specified kurtosis violates skewness kurtosis relationship
+ minkurt = matrix_power(C, 4) * matrix_power(solve((matrix_power(C, 3)), x_skew), 2)
+
+ if (np.sum(x_kurt < minkurt)):
+ warnings.warn('Bad Human Error: Kurtosis does not correspond to a distribution')
+ for i in range(len(x_kurt)):
+ if x_kurt[i] < minkurt[i]:
+ x_kurt[i] = 1.001* minkurt[i]
+
+ # Handle the arguments for lower bounds and upper bounds
+ if lb == None: # If lower bound is not specified manually set lower bound as -inf
+ lb = -np.inf * np.ones(n)
+
+ if ub == None: # If lower bound is not specified manually set lower bound as -inf
+ ub = np.inf * np.ones(n)
+
+ # Calculate parameters u and v
+ u = 0.5* ( -(solve(matrix_power(C, 3), x_skew) )
+ + np.sqrt(4 * solve(matrix_power(C, 4), x_kurt)
+ - 3 * matrix_power(solve(matrix_power(C, 3) , x_skew), 2)))
+ v = u + solve(matrix_power(C, 3), x_skew)
+ # Generate the sigma points
+ x0 = mu
+ x1 = mu - C * np.diag(u)
+ x2 = mu + C * np.diag(v)
+
+ # # --------------- This section handles the constraints ---------------
+ # Flag_constrain = 0 # Default flag to enforce constraint
+ # # Check if mean violates constraints
+ # if np.subtract(mu, lb).min() < 0 or np.subtract(mu, lb).min() < 0:
+ # Flag_constrain = 1 # Set flag to avoid enforcing state constraints
+ # warnings.warn('Unable fo enforce constraints: one or more of the mean does not satisfy lb < mean < ub')
+
+ # if Flag_constrain == 0:
+ # theta = 0.9; # Default value of user defined slack parameter
+
+ # # Ensure lower bound 'lb' is not violated
+ # Temp1 = np.subtract(np.hstack((x1, x2)), lb)
+ # L1 = np.nonzero(Temp1.min() < 0) # Find the location of sigma points that violate the lower bound
+ # Flag_calc = 0; # Flag that determines if skewness can be matched
+ # print(u)
+ # print(v)
+ # print(n)
+ # print(L1)
+ # for i in range(len(L1)):
+ # if L1[i] <= n:
+ # # Recalculate 'u' to satisfy lower bound 'lb'
+ # u[L1[i]] = theta * np.min(np.abs(np.subtract(mu, lb) / C[:, L1[i]]))
+ # else:
+ # # Recalculate 'v' to satisfy lower bound 'lb'
+ # print(L1[i])
+ # v[L1[i] - n] = theta * np.min(np.abs(np.subtract(mu, lb) / C[:, L1[i]-n]))
+ # Flag_calc = 1 # Set flag
+
+ # # Regenerate the sigma points
+ # x1 = mu - C * np.diag(u)
+ # x2 = mu + C * np.diag(v)
+
+ # # Ensure upper bound 'ub' is not violated
+ # Temp2 = ub - np.hstack((x1, x2))
+ # L2 = np.nonzero(min(Temp2) < 0) # Find the location of sigma points that
+ # # violate the upper bound
+ # for i in range(len(L2)):
+ # if L2(i) <= n:
+ # # Recalculate 'u' to satisfy upper bound 'ub'
+ # u[L2[i]] = theta * np.min(np.abs(np.subtract(mu, lb) / C[:, L1[i]]))
+ # else:
+ # # Recalculate 'v' to satisfy upper bound 'ub'
+ # v[L2[i] - v] = theta * np.min(np.abs(np.subtract(ub, mu) / C[:, L2[i]-n]))
+ # Flag_calc = 1 # Set flag
+
+ # if Flag_calc == 0:
+ # # Now recalculate parameter 'v' to match diagonal componen of
+ # # skewness tensor because it was 'v' was not previously redefined
+ # v = u + np.solve(matrix_power(C, 3), x_skew) # only done of v was not redefined
+
+ # # Regenerate the sigma points to reflect any change in 'u' or 'v'
+ # x1 = mu - C * np.diag(u)
+ # x2 = mu + C * np.diag(v)
+
+ # Recalculate weights to reflect any change in 'u' or 'v'
+ w2 = np.ones(n) / v / np.add(u, v)
+ w1 = w2 * v / u
+ # Output sigma point values
+ x = np.hstack((x0, x1, x2))
+ w0 = 1 - np.sum(np.vstack((w1, w2)), axis = 0)
+ weights = np.transpose(np.hstack((w0, np.transpose(w1), np.transpose(w2))))
+ # s = np.vstack((u, v))
+ # print(x)
+ # print(weights)
+ # print(s)
+ return x, weights
\ No newline at end of file
diff --git a/src/generalized_unsented_transform/random_variable_test.py b/src/generalized_unsented_transform/random_variable_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..eda6df0a3ee6845a7970943c985995059a084e35
--- /dev/null
+++ b/src/generalized_unsented_transform/random_variable_test.py
@@ -0,0 +1,42 @@
+import numpy as np
+from generalized_unscented_transform import generalized_ut
+from evaluate_from_sigma_points import Evaluate_sample_statistics
+
+
+def quadratic_transform(sigma_points):
+ sigma_points = np.array(sigma_points)[0]
+ y = []
+ for x in sigma_points:
+ y.append(3*x + 2*(x**2))
+ y = np.matrix(y)
+ return y
+
+
+# Gaussian case
+def test_Gaussian():
+ m1 = 1
+ m2 = 4
+ m3 = 0
+ m4 = 48
+ sigma_points, weights = generalized_ut(np.matrix([m1]), np.matrix([m2]), np.matrix([m3]), np.matrix([m4]))
+ sigma_mean, sigma_cov, sigma_skew, sigma_kurt = Evaluate_sample_statistics(quadratic_transform(sigma_points), weights)
+ print(sigma_mean)
+ print(sigma_cov)
+ print(sigma_skew)
+ print(sigma_kurt)
+
+def test_Exp():
+ m1 = 0.5
+ m2 = 0.25
+ m3 = 0.25
+ m4 = 0.5625
+ sigma_points, weights = generalized_ut(np.matrix([m1]), np.matrix([m2]), np.matrix([m3]), np.matrix([m4]))
+ sigma_mean, sigma_cov, sigma_skew, sigma_kurt = Evaluate_sample_statistics(quadratic_transform(sigma_points), weights)
+ print(sigma_mean)
+ print(sigma_cov)
+ print(sigma_skew)
+ print(sigma_kurt)
+
+
+if __name__ == "__main__":
+ test_Exp()