html5フォームにおけるrequired属性の使用について

import numpy as np
import sklearn.cross_decomposition
pls2 = sklearn.cross_decomposition.PLSRegression()
xx = np.random.random((5,5))
yy = np.zeros((5,5) )
yy[0,:] = [0,1,0,0,0]
yy[1,:] = [0,0,0,1,0]
yy[2,:] = [0,0,0,0,1]
#yy[3,:] = [1,0,0,0,0] # Durch das Freigeben dieser Zeile wird das Problem gelöst
pls2.fit(xx, yy)

C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:44: RuntimeWarning: invalid value encountered in divide
x_weights = np.dot(X.T, y_score) / np.dot(y_score.T, y_score)
C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:64: RuntimeWarning: ungültiger Wert in less gefunden
if np.dot(x_weights_diff.T, x_weights_diff) < tol oder Y.shape[1] == 1:
C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:67: UserWarning: Maximale Anzahl von Iterationen erreicht
warnings.warn('Maximale Anzahl von Iterationen erreicht')
C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:297: RuntimeWarning: ungültiger Wert in less gefunden
if np.dot(x_scores.T, x_scores) < np.finfo(np.double).eps:
C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:275: RuntimeWarning: ungültiger Wert in less gefunden
if np.all(np.dot(Yk.T, Yk) < np.finfo(np.double).eps):
Traceback (most recent call last):
Datei "C:\svn\hw4\code\test_plsr2.py", Zeile 8, in
pls2.fit(xx, yy)
Datei "C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py", Zeile 335, in fit
linalg.pinv(np.dot(self.x_loadings_.T, self.x_weights_)))
Datei "C:\Anaconda\lib\site-packages\scipy\linalg\basic.py", Zeile 889, in pinv
a = _asarray_validated(a, check_finite=check_finite)
Datei "C:\Anaconda\lib\site-packages\scipy\_lib\_util.py", Zeile 135, in _asarray_validated
a = np.asarray_chkfinite(a)
Datei "C:\Anaconda\lib\site-packages\numpy\lib\function_base.py", Zeile 613, in asarray_chkfinite
"array darf keine Infs oder NaNs enthalten")
ValueError: array darf keine infs oder NaNs enthalten

np.isnan(xx).any()
np.isnan(yy).any()
np.isinf(xx).any()
np.isinf(yy).any()

xx = np.nan_to_num(xx)
yy = np.nan_to_num(yy)

  <form method="post" action="" novalidate>
		   username<input type="text" id="username" required="required"><br>
		   password<input type="password" id="password" required><br>
		   <input type="submit" value="login" >
	   </form>