Add Python scripts to minimize percentage error.
Finds the coefficient(s) to multiply the estimated bitrate by
to minimize the percentage error between estimated and actual bitrate.
BUG=aomedia:3045
Change-Id: I76d70b54b0ed5e71bc92383db53f794979cc7f46
diff --git a/tools/gop_bitrate/python/bitrate_accuracy_percentage_error.py b/tools/gop_bitrate/python/bitrate_accuracy_percentage_error.py
new file mode 100644
index 0000000..fcde9797
--- /dev/null
+++ b/tools/gop_bitrate/python/bitrate_accuracy_percentage_error.py
@@ -0,0 +1,106 @@
+import numpy as np
+
+# Finds the coefficient to multiply A by to minimize
+# the percentage error between A and B.
+def minimize_percentage_error_model_a(A, B):
+ z = np.where(B == 0)[0]
+ A = np.delete(A, z, axis=0)
+ B = np.delete(B, z, axis=0)
+ z = np.where(A == 0)[0]
+ A = np.delete(A, z, axis=0)
+ B = np.delete(B, z, axis=0)
+
+ R = np.divide(A, B)
+ num = 0
+ den = 0
+ for r_i in R:
+ num += r_i
+ den += r_i**2
+ if den == 0:
+ x = 0
+ else:
+ x = (num / den)[0]
+ return x
+
+def minimize_percentage_error_model_b(r_e, r_m, r_f):
+ z = np.where(r_f == 0)[0]
+ r_e = np.delete(r_e, z, axis=0)
+ r_m = np.delete(r_m, z, axis=0)
+ r_f = np.delete(r_f, z, axis=0)
+
+ r_ef = np.divide(r_e, r_f)
+ r_mf = np.divide(r_m, r_f)
+ sum_ef = np.sum(r_ef)
+ sum_ef_sq = np.sum(np.square(r_ef))
+ sum_mf = np.sum(r_mf)
+ sum_mf_sq = np.sum(np.square(r_mf))
+ sum_ef_mf = np.sum(np.multiply(r_ef, r_mf))
+
+ # Divides x by y. If y is zero, returns 0.
+ divide = lambda x, y : 0 if y == 0 else x / y
+
+ # Set up and solve the matrix equation
+ A = np.array([[1, divide(sum_ef_mf, sum_ef_sq)],[divide(sum_ef_mf, sum_mf_sq), 1]])
+ B = np.array([divide(sum_ef, sum_ef_sq), divide(sum_mf, sum_mf_sq)])
+ A_inv = np.linalg.pinv(A)
+ x = np.matmul(A_inv, B)
+ return x
+
+# Calculates the average percentage error between A and B.
+def average_error_model_a(A, B, x):
+ error = 0
+ for i, a in enumerate(A):
+ a = a[0]
+ b = B[i][0]
+ if b == 0:
+ continue
+ error += abs(x*a - b) / b
+ error *= 100
+ error /= A.shape[0]
+ return error
+
+def average_error_model_b(A, M, B, x):
+ error = 0
+ for i, a in enumerate(A):
+ a = a[0]
+ mv = M[i]
+ b = B[i][0]
+ if b == 0:
+ continue
+ estimate = x[0]*a
+ estimate += x[1]*mv
+ error += abs(estimate - b) / b
+ error *= 100
+ error /= A.shape[0]
+ return error
+
+# Traverses the data and prints out one value for
+# each update type.
+def print_solutions(file_path):
+ data = np.genfromtxt(file_path, delimiter="\t")
+
+ prev_update = 0
+ split_list_indices = list()
+ for i, val in enumerate(data):
+ if prev_update != val[3]:
+ split_list_indices.append(i)
+ prev_update = val[3]
+
+ split = np.split(data, split_list_indices)
+
+ for array in split:
+ A, mv, B, update = np.hsplit(array, 4)
+ print("update type:", update[0][0])
+ xa = minimize_percentage_error_model_a(A, B)
+ xb = minimize_percentage_error_model_b(A, mv, B)
+ print("Model A coefficients:", xa, " | Model B coefficients:", xb)
+ error_a = average_error_model_a(A, B, xa)
+ error_b = average_error_model_b(A, mv, B, xb)
+ baseline_error_a = average_error_model_a(A, B, 1)
+ baseline_error_b = average_error_model_b(A, mv, B, [1, 1])
+ print("error a:", error_a, " | error b:", error_b)
+ print("baseline error a:", baseline_error_a, "baseline error b:", baseline_error_b)
+ print()
+
+if __name__ == "__main__":
+ print_solutions("data2/lowres_17f_target150_data.txt")
