# This is an accuracy result for various hybrid embedding
import numpy as np

######### Angular Hybrid 2 #########
AE12_1 = np.array([0.8822695035460993, 0.9744680851063829, 0.9536643026004729, 0.9891252955082742, 0.9593380614657211])
AE12_2 = np.array([0.9621749408983451, 0.9862884160756501, 0.8884160756501182, 0.9702127659574468, 0.9527186761229315])
AE12_3 = np.array([0.9328605200945627, 0.9366430260047282, 0.9791962174940898, 0.9569739952718677, 0.9375886524822695])
AE12_4 = np.array([0.9773049645390071, 0.9612293144208038, 0.9494089834515367, 0.9385342789598109, 0.8888888888888888])

PCA12_1 = np.array([0.9843971631205674, 0.9810874704491725, 0.9735224586288416, 0.984869976359338, 0.9839243498817967])
PCA12_2 = np.array([0.9843971631205674, 0.984869976359338, 0.9725768321513002, 0.9829787234042553, 0.9839243498817967])
PCA12_3 = np.array([0.9763593380614657, 0.9810874704491725, 0.9810874704491725, 0.9815602836879432, 0.9839243498817967])
PCA12_4 = np.array([0.9617021276595744, 0.9815602836879432, 0.9791962174940898, 0.9825059101654846, 0.9806146572104019])

AE12_1_mean, AE12_1_std = AE12_1.mean(), AE12_1.std()
AE12_2_mean, AE12_2_std = AE12_2.mean(), AE12_2.std()
AE12_3_mean, AE12_3_std = AE12_3.mean(), AE12_3.std()
AE12_4_mean, AE12_4_std = AE12_4.mean(), AE12_4.std()

PCA12_1_mean, PCA12_1_std = PCA12_1.mean(), PCA12_1.std()
PCA12_2_mean, PCA12_2_std = PCA12_2.mean(), PCA12_2.std()
PCA12_3_mean, PCA12_3_std = PCA12_3.mean(), PCA12_3.std()
PCA12_4_mean, PCA12_4_std = PCA12_4.mean(), PCA12_4.std()

######### Amplitude Hybrid 2 #########
AE16_1 = np.array([0.9947990543735225, 0.9612293144208038, 0.9319148936170213, 0.9626477541371158, 0.9905437352245863])
AE16_2 = np.array([0.9328605200945627, 0.9938534278959811, 0.9593380614657211, 0.9479905437352246, 0.9938534278959811])
AE16_3 = np.array([0.9356973995271868, 0.984869976359338, 0.9460992907801419, 0.9801418439716312, 0.9815602836879432])
AE16_4 = np.array([0.932387706855792, 0.9650118203309692, 0.984869976359338, 0.9484633569739953, 0.9290780141843972])

PCA16_1 = np.array([0.775886524822695, 0.7621749408983451, 0.7602836879432624, 0.7021276595744681, 0.8033096926713948])
PCA16_2 = np.array([0.7621749408983451, 0.7267139479905438, 0.7947990543735225, 0.7460992907801418, 0.7536643026004728])
PCA16_3 = np.array([0.7087470449172577, 0.7924349881796691, 0.7219858156028369, 0.7773049645390071, 0.7692671394799054])
PCA16_4 = np.array([0.7210401891252955, 0.7569739952718676, 0.7016548463356974, 0.7635933806146572, 0.7683215130023641])

AE16_1_mean, AE16_1_std = AE16_1.mean(), AE16_1.std()
AE16_2_mean, AE16_2_std = AE16_2.mean(), AE16_2.std()
AE16_3_mean, AE16_3_std = AE16_3.mean(), AE16_3.std()
AE16_4_mean, AE16_4_std = AE16_4.mean(), AE16_4.std()

PCA16_1_mean, PCA16_1_std = PCA16_1.mean(), PCA16_1.std()
PCA16_2_mean, PCA16_2_std = PCA16_2.mean(), PCA16_2.std()
PCA16_3_mean, PCA16_3_std = PCA16_3.mean(), PCA16_3.std()
PCA16_4_mean, PCA16_4_std = PCA16_4.mean(), PCA16_4.std()
######### Angular Hybrid 4 #########

AE30_1 = np.array([0.8832151300236407, 0.8581560283687943, 0.941371158392435, 0.8132387706855791, 0.9352245862884161])
AE30_2 = np.array([0.9016548463356974, 0.9281323877068558, 0.8033096926713948, 0.9177304964539007, 0.8917257683215131])
AE30_3 = np.array([0.9186761229314421, 0.8988179669030733, 0.9427895981087471, 0.9125295508274232, 0.9177304964539007])
AE30_4 = np.array([0.9304964539007092, 0.9177304964539007, 0.9035460992907801, 0.950354609929078, 0.9144208037825059])

PCA30_1 = np.array([0.9843971631205674, 0.9650118203309692, 0.9763593380614657, 0.983451536643026, 0.9229314420803783])
PCA30_2 = np.array([0.9843971631205674, 0.9697399527186761, 0.9735224586288416, 0.9768321513002364, 0.983451536643026])
PCA30_3 = np.array([0.9820330969267139, 0.9645390070921985, 0.966903073286052, 0.9829787234042553, 0.9820330969267139])
PCA30_4 = np.array([0.9645390070921985, 0.9569739952718677, 0.9791962174940898, 0.9749408983451536, 0.9777777777777777])

AE30_1_mean, AE30_1_std = AE30_1.mean(), AE30_1.std()
AE30_2_mean, AE30_2_std = AE30_2.mean(), AE30_2.std()
AE30_3_mean, AE30_3_std = AE30_3.mean(), AE30_3.std()
AE30_4_mean, AE30_4_std = AE30_4.mean(), AE30_4.std()

PCA30_1_mean, PCA30_1_std = PCA30_1.mean(), PCA30_1.std()
PCA30_2_mean, PCA30_2_std = PCA30_2.mean(), PCA30_2.std()
PCA30_3_mean, PCA30_3_std = PCA30_3.mean(), PCA30_3.std()
PCA30_4_mean, PCA30_4_std = PCA30_4.mean(), PCA30_4.std()

######### Amplitude Hybrid 4 #########

AE32_1 = np.array([0.9588652482269504, 0.9186761229314421, 0.9366430260047282, 0.9919621749408983, 0.9862884160756501])
AE32_2 = np.array([0.9825059101654846, 0.9891252955082742, 0.9791962174940898, 0.9858156028368794, 0.9900709219858156])
AE32_3 = np.array([0.9914893617021276, 0.9730496453900709, 0.9602836879432625, 0.9593380614657211, 0.9635933806146572])
AE32_4 = np.array([0.9711583924349881, 0.9763593380614657, 0.902127659574468, 0.9773049645390071, 0.9735224586288416])

PCA32_1 = np.array([0.791016548463357, 0.7867612293144208, 0.7749408983451537, 0.723404255319149, 0.798581560283688])
PCA32_2 = np.array([0.7153664302600473, 0.735224586288416, 0.7063829787234043, 0.7059101654846336, 0.7063829787234043])
PCA32_3 = np.array([0.8080378250591016, 0.7470449172576832, 0.7924349881796691, 0.7711583924349882, 0.7924349881796691])
PCA32_4 = np.array([0.7867612293144208, 0.8037825059101655, 0.7078014184397163, 0.750354609929078, 0.7825059101654847])

AE32_1_mean, AE32_1_std = AE32_1.mean(), AE32_1.std()
AE32_2_mean, AE32_2_std = AE32_2.mean(), AE32_2.std()
AE32_3_mean, AE32_3_std = AE32_3.mean(), AE32_3.std()
AE32_4_mean, AE32_4_std = AE32_4.mean(), AE32_4.std()

PCA32_1_mean, PCA32_1_std = PCA32_1.mean(), PCA32_1.std()
PCA32_2_mean, PCA32_2_std = PCA32_2.mean(), PCA32_2.std()
PCA32_3_mean, PCA32_3_std = PCA32_3.mean(), PCA32_3.std()
PCA32_4_mean, PCA32_4_std = PCA32_4.mean(), PCA32_4.std()

###########
print("PCA12-1: " + str(PCA12_1_mean) + " " + str(PCA12_1_std))
print("PCA12-2: " + str(PCA12_2_mean) + " " + str(PCA12_2_std))
print("PCA12-3: " + str(PCA12_3_mean) + " " + str(PCA12_3_std))
print("PCA12-4: " + str(PCA12_4_mean) + " " + str(PCA12_4_std))

print("PCA16-1: " + str(PCA16_1_mean) + " " + str(PCA16_1_std))
print("PCA16-2: " + str(PCA16_2_mean) + " " + str(PCA16_2_std))
print("PCA16-3: " + str(PCA16_3_mean) + " " + str(PCA16_3_std))
print("PCA16-4: " + str(PCA16_4_mean) + " " + str(PCA16_4_std))

print("PCA30-1: " + str(PCA30_1_mean) + " " + str(PCA30_1_std))
print("PCA30-2: " + str(PCA30_2_mean) + " " + str(PCA30_2_std))
print("PCA30-3: " + str(PCA30_3_mean) + " " + str(PCA30_3_std))
print("PCA30-4: " + str(PCA30_4_mean) + " " + str(PCA30_4_std))

print("PCA32-1: " + str(PCA32_1_mean) + " " + str(PCA32_1_std))
print("PCA32-2: " + str(PCA32_2_mean) + " " + str(PCA32_2_std))
print("PCA32-3: " + str(PCA32_3_mean) + " " + str(PCA32_3_std))
print("PCA32-4: " + str(PCA32_4_mean) + " " + str(PCA32_4_std))


print("AE12-1: " + str(AE12_1_mean) + " " + str(AE12_1_std))
print("AE12-2: " + str(AE12_2_mean) + " " + str(AE12_2_std))
print("AE12-3: " + str(AE12_3_mean) + " " + str(AE12_3_std))
print("AE12-4: " + str(AE12_4_mean) + " " + str(AE12_4_std))

print("AE16-1: " + str(AE16_1_mean) + " " + str(AE16_1_std))
print("AE16-2: " + str(AE16_2_mean) + " " + str(AE16_2_std))
print("AE16-3: " + str(AE16_3_mean) + " " + str(AE16_3_std))
print("AE16-4: " + str(AE16_4_mean) + " " + str(AE16_4_std))

print("AE30-1: " + str(AE30_1_mean) + " " + str(AE30_1_std))
print("AE30-2: " + str(AE30_2_mean) + " " + str(AE30_2_std))
print("AE30-3: " + str(AE30_3_mean) + " " + str(AE30_3_std))
print("AE30-4: " + str(AE30_4_mean) + " " + str(AE30_4_std))

print("AE32-1: " + str(AE32_1_mean) + " " + str(AE32_1_std))
print("AE32-2: " + str(AE32_2_mean) + " " + str(AE32_2_std))
print("AE32-3: " + str(AE32_3_mean) + " " + str(AE32_3_std))
print("AE32-4: " + str(AE32_4_mean) + " " + str(AE32_4_std))


