ich habe Probleme bei einer Abgabe.
In this assignment you will estimate cognitive states from electroencephalogram (EEG) data. The data matrix X contains 5 selected time windows of EEG activity at 62 electrodes after a visual stimulus was presented on the screen in front of the subject. If the first row of 𝑌 is 1, the stimulus was a target stimulus, if the second row of 𝑌 is 1, the stimulus was a non-target stimulus.
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import pylab as pl
import scipy as sp
from scipy.linalg import eig
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
def load_data(fname):
# load the data
data = loadmat(fname)
# extract images and labels
X = data['X']
Y = data['Y']
# collapse the time-electrode dimensions
X = sp.reshape(X,(X.shape[0]*X.shape[1],X.shape[2])).T
# transform the labels to (-1,1)
Y = sp.sign((Y[0,:]>0) -.5)
return X,Y
X,Y = load_data(fname='bcidata.mat')Code: Alles auswählen
def ncc_fit(X, Y):
'''
Train a nearest centroid classifier for N data points in D dimensions
Input:
X N-by-D Data Matrix
Y label vector of length N, labels are -1 or 1
Output:
w weight vector of length D
b bias vector of length D
'''
# class means
# IMPLEMENT CODE HERE
mupos =
muneg =
w =
b = (w.dot(mupos) + w.dot(muneg))/2.
# return the weight vector
return w,b
X_train, X_test, Y_train, Y_test = train_test_split(X,Y)
w_ncc, b_ncc = ncc_fit(X_train, Y_train)
pl.hist(X_test[Y_test<0, :] @ w_ncc)
pl.hist(X_test[Y_test>0, :] @ w_ncc)
pl.plot([b_ncc, b_ncc], [0, 500], color='k')
pl.xlabel('$Xw_{NCC}$')
pl.legend(('$b_{ncc}$','non-target','target'))
pl.ylim([0, 450])
acc = int((sp.sign(X_test @ w_ncc - b_ncc)==Y_test).mean()*100)
pl.title(f"NCC Acc {acc}%");Train a linear discriminant classifier and compare it with the NCC one.
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def lda_fit(X,Y):
'''
Train a Linear Discriminant Analysis classifier
Input:
X N-by-D Data Matrix
Y label vector of length N, labels are -1 or 1
Output:
w weight vector of length D
b bias vector of length D
'''
# class means
# IMPLEMENT CODE HERE
mupos = ...
muneg = ...
# D-by-D inter class covariance matrix (signal)
Sinter = ...
# D-by-D intra class covariance matrices (noise)
Sintra = ...
# solve eigenproblem
eigvals, eigvecs = sp.linalg.eig(Sinter,Sintra)
w = eigvecs[:,eigvals.argmax()]
# bias term
b = (w.dot(mupos) + w.dot(muneg))/2.
# return the weight vector
return w,b