--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tests/test_linear_regression.py	Sun Dec 17 14:26:15 2017 -0800
@@ -0,0 +1,85 @@
+"""
+test linear regression
+"""
+
+import csv
+import math
+import os
+import numpy as np
+import random
+from tvii import linear_regression
+from tvii.noise import add_noise
+
+
+def test_linear_regression():
+    """Make sure we can do `W*x + b = y` properly"""
+
+    # training data:  exact fit, W=-1, b=1
+    x_train = [1,2,3,4]
+    y_train = [0,-1,-2,-3]
+
+    # our guesses
+    W_guess = 0.  # Why not?  Be bold
+    b_guess = 0.
+
+    # perform the regression
+    W, b, loss  = linear_regression.linear_regression(x_train,
+                                                      y_train,
+                                                      W_guess=W_guess,
+                                                      b_guess=b_guess)
+    # make sure we're close
+    W_exact = -1.
+    b_exact = 1.
+
+    assert abs(W - W_exact) < 1e-5
+    assert abs(b - b_exact) < 1e-5
+
+
+def test_linear_regression_noisy():
+    """
+    Make sure we can do `W*x + b = y` with some noise
+    """
+
+    # start simple
+    slope = 1.5  # rises 3 every 2
+    intercept = random.random() * 5.
+    line = lambda x: slope*x + intercept
+
+    # make range
+    # TODO: np.linspace(-10., 10, 100)
+    xspan = (-10., 10.)
+    npoints = 100
+    dx = (xspan[-1] - xspan[0])/(npoints-1.)
+    xi = [xspan[0]+dx*i
+          for i in range(npoints)]
+
+    # add some noise to it
+    x = add_noise(xi, fraction=0.01)
+    assert len(x) == len(xi)
+    assert x != xi
+    assert x == sorted(x)
+
+    # calculate true y
+    truey = [line(xx) for xx in x]
+
+    # add some noise to that
+    y = add_noise(truey, fraction=0.01)
+    assert len(y) == len(truey)
+
+    # you're now all set up for your regression
+    W, b, loss  = linear_regression.linear_regression(x, y)
+
+    # Show us what you got!
+    # TODO: this gives nan for both `W` and `b`
+    # The lines loop okay so I'm guessing some sort of
+    # numerical instability
+    try:
+        assert W == slope  # XXX shouldn't be exactly equal anyway
+    except AssertionError:
+        dumpfile = os.environ.get('NETTWERK_FAILURE')
+        if dumpfile:
+            # dump the points
+            with open(dumpfile, 'w') as f:
+                writer = csv.writer(f)
+                writer.writerows(zip(x, y))
+        pass  # XXX ignoring true negative :(