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import numpy as np
import scipy.io
import os
def PSTAAP_feature(protein_sequences, test_PSTAAP=False):
for i in range(len(protein_sequences)):
protein_sequences[i] = protein_sequences[i][:24] + protein_sequences[i][25:]
if test_PSTAAP:
mat_contents = scipy.io.loadmat("Feature_extraction_algorithms/Fr_test.mat")
else:
mat_contents = scipy.io.loadmat("Feature_extraction_algorithms/Fr_train.mat")
Fr = mat_contents['Fr']
"""
print(Fr[0*400+5*20+0,0])
print(Fr[5 * 400 + 0 * 20 + 16, 1])
print(Fr[0 * 400 + 16 * 20 + 14, 2])
"""
AA = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
PSTAAP = np.zeros((len(protein_sequences), 46))
for i in range(len(protein_sequences)):
for j in range(len(protein_sequences[0])-2):
t1 = protein_sequences[i][j]
position1 = AA.index(t1)
t2 = protein_sequences[i][j+1]
position2 = AA.index(t2)
t3 = protein_sequences[i][j+2]
position3 = AA.index(t3)
PSTAAP[i][j] = Fr[400 * position1 + 20 * position2 + position3][j]
return PSTAAP
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import splrep, BSpline
from sklearn.preprocessing import MinMaxScaler
from numpy.polynomial import Polynomial
def plot_multiple_polynomial_fitted_functions(sample_datas, degree=3):
markers = ["o", "o", "^", "^", "v", "p"]
colors = ["b", "b", "c", "c", "m", "y"]
label = ["sample1(1,0,0,0)", "sample1(1,0,0,0)", "sample1(0,1,0,0)", "sample2(0,1,0,0)", "sample3(0,0,1,0)", "sample6(0,0,0,1)"]
plt.figure(figsize=(12, 6))
for i, sample_data in enumerate(sample_datas):
if i == 0 or i == 1 or i == 4 or i == 5:
continue
# 无量纲化处理
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(sample_data.reshape(-1, 1)).flatten()
# 拟合多项式函数
x = np.linspace(0, 1, len(normalized_data))
p = Polynomial.fit(x, normalized_data, degree)
y_poly = p(x)
# 计算极值点
dy_poly = p.deriv(1)(x)
extrema_indices = np.where(np.diff(np.sign(dy_poly)))[0]
extrema_x = x[extrema_indices]
extrema_y = y_poly[extrema_indices]
plt.plot(x, y_poly, label=f'{label[i]}', marker=markers[i], color=colors[i])
plt.plot(extrema_x, extrema_y, 'rx', markersize=10) # 标记极值点
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Fitted Polynomial Functions with Extrema')
plt.legend()
plt.show()
def plot_multiple_fitted_functions(sample_datas, smooth_factor=1):
markers = ["o", "o", "^", "^", "v", "p"]
colors = ["b", "b", "c", "c", "m", "y"]
label = ["", "", "sample1(0,1,0,0)", "sample2(0,1,0,0)", "sample3(0,0,1,0)", ""]
plt.figure(figsize=(12, 6))
for i, sample_data in enumerate(sample_datas):
if i == 0 or i == 1 or i == 4 or i == 5:
continue
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(sample_data.reshape(-1, 1)).flatten()
x = np.linspace(0, 1, len(normalized_data))
tck = splrep(x, normalized_data, k=3, s=smooth_factor)
spline = BSpline(tck[0], tck[1], tck[2])
y_spline = spline(x)
dy_spline = spline.derivative()
extrema_indices = np.where(np.diff(np.sign(dy_spline(x))))[0]
extrema_x = x[extrema_indices]
extrema_y = y_spline[extrema_indices]
plt.plot(x, y_spline, label=f'{label[i]}', marker=markers[i], color=colors[i])
plt.plot(extrema_x, extrema_y, 'rx', markersize=10)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Fitted B-Spline Functions with Extrema')
plt.legend()
plt.show()
protein_sequences = [
"TSPASVASSSSTPSSKTKDLGHNDKSSTPGLKSNTPTPRNDAPTPGTST", # a
"LGGNIEQLVARSNILTLMYQCMQDKMPEVRQSSFALLGDLTKACFQHVK", # a
"VDFQHASEDARKTINQWVKGQTEGKIPELLASGMVDNMTKLVLVNAIYF", # c
"VEGTLKGPEVDLKGPRLDFEGPDAKLSGPSLKMPSLEISAPKVTAPDVD", # c
"IDILTSREQFFSDEERKYMAINQKKAYILVTPLKSRKVIEQRCMRYNLS", # m
"LAGTDGETTTQGLDGLSERCAQYKKDGADFAKWRCVLKISERTPSALAI", # s
]
data = PSTAAP_feature(protein_sequences, False)
# 调用绘图函数
plot_multiple_polynomial_fitted_functions(data)
plot_multiple_fitted_functions(data)
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