moved a lot of files to folders that will mesh with the directory structure intended...

[swc-testing-nose.git] / 4-SoftwareEngineering / Testing / close_line.py

diff --git a/4-SoftwareEngineering/Testing/close_line.py b/4-SoftwareEngineering/Testing/close_line.py
deleted file mode 100644 (file)
index a6a5c5e..0000000
--- a/4-SoftwareEngineering/Testing/close_line.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import numpy as np
-from scipy.optimize import fmin
-
-#
-# Attempt 1
-#
-
-def point_on_line1(x, p1, p2):
-    y = p1[1] + (x - p1[0])*(p2[1] - p1[1]) / (p2[0] - p1[0])
-    return np.array([x, y])
-
-
-def dist_from_line1(x, pdata, p1, p2):
-    pline = point_on_line1(x, p1, p2)
-    return np.sqrt(np.sum((pline - pdata)**2))
-
-
-def closest_data_to_line1(data, p1, p2):
-    dists = np.empty(len(data), dtype=float)
-    for i, pdata in enumerate(data):
-        x = fmin(dist_from_line1, p1[0], (pdata, p1, p2), disp=False)[0]
-        dists[i] = dist_from_line1(x, pdata, p1, p2)
-    imin = np.argmin(dists)
-    return imin, data[imin]
-
-
-#
-# Attempt 2
-#
-
-def dist_from_line2(pdata, p1, p2):
-    a = np.sqrt(np.sum((p1 - pdata)**2))
-    b = np.sqrt(np.sum((p2 - pdata)**2))
-    c = np.sqrt(np.sum((p2 - p1)**2))
-    h = a * np.sqrt(1.0 - ((a**2 + c**2 - b**2) / (2.0 * a * c))**2)
-    return h
-
-def closest_data_to_line2(data, p1, p2):
-    dists = np.empty(len(data), dtype=float)
-    for i, pdata in enumerate(data):
-        dists[i] = dist_from_line2(pdata, p1, p2)
-    imin = np.argmin(dists)
-    return imin, data[imin]
-
-#
-# Attempt 3
-#
-
-def perimeter3(pdata, p1, p2):
-    a = np.sqrt(np.sum((p1 - pdata)**2))
-    b = np.sqrt(np.sum((p2 - pdata)**2))
-    c = np.sqrt(np.sum((p2 - p1)**2))
-    return (a + b + c)
-
-def closest_data_to_line3(data, p1, p2):
-    peris = np.empty(len(data), dtype=float)
-    for i, pdata in enumerate(data):
-        peris[i] = perimeter3(pdata, p1, p2)
-    imin = np.argmin(peris)
-    return imin, data[imin]
-
-#
-# Attempt 4
-#
-
-def closest_data_to_line4(data, p1, p2):
-    return data[np.argmin(np.sqrt(np.sum((p1 - data)**2, axis=1)) + \
-                np.sqrt(np.sum((p2 - data)**2, axis=1)))]
-