[ENH] Radviz: new widget

Orange/projection/__init__.py

@@ -3,3 +3,4 @@
 from .cur import *
 from .manifold import *
 from .freeviz import *
+from .radviz import radviz

Orange/projection/radviz.py

@@ -0,0 +1,45 @@
+import numpy as np
+
+from Orange.data import Domain
+
+
+def radviz(data, attrs, points=None):
+    x = data.transform(domain=Domain(attrs)).X
+    mask = ~np.isnan(x).any(axis=1)
+    x = x[mask]
+
+    n = len(x)
+    if not n:
+        return None, None, mask
+    x = normalize(x)
+
+    r_x = np.zeros(n)
+    r_y = np.zeros(n)
+
+    m = x.shape[1]
+    if points is not None:
+        s = points[:, :2]
+    else:
+        s = np.array([(np.cos(t), np.sin(t))
+                      for t in [2.0 * np.pi * (i / float(m))
+                                for i in range(m)]])
+    for i in range(n):
+        row = x[i]
+        row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)
+        with np.errstate(divide='ignore', invalid='ignore'):
+            a = (s * row_).sum(axis=0)
+            b = row.sum()
+            y = np.divide(a, b, out=np.zeros_like(a), where=b != 0)
+        r_x[i] = y[0]
+        r_y[i] = y[1]
+
+    return np.stack((r_x, r_y), axis=1), np.column_stack((s, attrs)), mask
+
+
+def normalize(x):
+    """
+    MinMax normalization to fit a matrix in the space [0,1] by column.
+    """
+    a = x.min(axis=0)
+    b = x.max(axis=0)
+    return (x - a[np.newaxis, :]) / ((b - a)[np.newaxis, :])