StandardScaler in sklearn not fitting properly, or is it?
<p>I am using <code>StandardScalar</code> from <code>sklearn</code> to scale my feature vector, but it doesn't seem to fit the training feature vector properly. Or maybe it is expected behavior, but if it is, could someone explain why (preferably with some mathematical explanation too).</p>

<pre><code>from sklearn.preprocessing import StandardScaler
import numpy as np

scale_inst = StandardScaler()

# train feature vector
x1 = np.array([1, 2, 10, 44, 55])
# test feature vector
x2 = np.array([1, 2, 10, 44, 667])

# first I fit
scale_inst.fit(x1)
# than I transform training vector and test vector
print scale_inst.transform(x1)
print scale_inst.transform(x2)

# OUTPUT
[-0.94627295 -0.90205459 -0.54830769  0.95511663  1.44151861]
[ -0.94627295  -0.90205459  -0.54830769   0.95511663  28.50315638]
</code></pre>

<p>Why does it scale 667 to 28.50315638, shouldn't it be scaled to 1.44151861, aka the max value of the training feature vector?</p>