# python – ValueError：x_new中的值低于插值范围

`my_estimator = LassoLarsCV(fit_intercept=False, normalize=False, positive=True, max_n_alphas=1e5)`

`my_estimator.fit(x, y)`

```File "/usr/lib64/python2.7/site-packages/sklearn/linear_model/least_angle.py", line 1113, in fit
axis=0)(all_alphas)
File "/usr/lib64/python2.7/site-packages/scipy/interpolate/polyint.py", line 79, in __call__
y = self._evaluate(x)
File "/usr/lib64/python2.7/site-packages/scipy/interpolate/interpolate.py", line 498, in _evaluate
out_of_bounds = self._check_bounds(x_new)
File "/usr/lib64/python2.7/site-packages/scipy/interpolate/interpolate.py", line 525, in _check_bounds
raise ValueError("A value in x_new is below the interpolation "
ValueError: A value in x_new is below the interpolation range.```

```import numpy
import sklearn.linear_model

# create 40000 x 40 sample data from linear model with a bit of noise
npoints = 40000
ndims = 40
numpy.random.seed(1)
X = numpy.random.random((npoints, ndims))
w = numpy.random.random(ndims)
y = X.dot(w) + numpy.random.random(npoints) * 0.1

clf = sklearn.linear_model.LassoLarsCV(fit_intercept=False, normalize=False, max_n_alphas=1e6)
clf.fit(X, y)

# coefficients are almost exactly recovered, this prints 0.00377
print max(abs( clf.coef_ - w ))

# alphas actually used are 41 or ndims+1
print clf.alphas_.shape```

alphas_ array, shape (n_alphas + 1,)

Maximum of covariances (in
absolute value) at each iteration. n_alphas is either max_iter,
n_features, or the number of nodes in the path with correlation
greater than alpha, whichever is smaller.