def _median(x, inplace):
assert(x.ndim == 1)
n = x.shape[0]
if n > 3:
k = n >> 1
s = select(x, k, inplace=inplace)
if n & 1:
return s[k]
else:
return 0.5*(s[k]+s[:k].max())
elif n == 0:
return np.nan
elif n == 2:
return 0.5*(x[0]+x[1])
else: # n == 3
s = select(x, 1, inplace=inplace)
return s[1]
def pred_knn(self, sample):
distances = [euclidean(x, sample) for x in self.X_train]
tmp_dst = distances[:]
k_smallest = q.select(tmp_dst, 0, len(tmp_dst) - 1, self.k)
labels = [
self.Y_train[i] for i, dist in enumerate(distances)
if dist < k_smallest
]
return max(set(labels), key=labels.count)
def predict(self, X_test):
predictions = []
for sample in X_test:
distances = [euclidean(x, sample) for x in self.X_train]
# indices = np.array(distances).argsort()[:self.k]
# labels = [Y_train[index] for index in indices]
tmp_dst = distances[:]
k_smallest = q.select(tmp_dst, 0, len(tmp_dst) - 1, self.k)
labels = [
self.Y_train[i] for i, dist in enumerate(distances)
if dist < k_smallest
]
predictions.append(max(set(labels), key=labels.count))
return predictions
def partition(self, points, start, end, cutdim, minx, miny, maxx, maxy):
n = kdnode();
n.st = start;
n.end = end;
n.minx = minx;
n.miny = miny;
n.width = maxx-minx;
n.height = maxy-miny;
if ( cutdim >= 2 ):
cutdim = 0;
if ( (end-start) > self.maxbinsz ):
med = select(points, int( (start+end)/2 )-start, cutdim, start, end-1);
n.dim = cutdim;
n.val = med;
## Plotting ##
s = [minx, miny];
e = [maxx, maxy];
s[n.dim] = n.val[n.dim];
e[n.dim] = n.val[n.dim];
plt.plot([s[0], e[0]], [s[1], e[1]]);
self.fig.canvas.draw();
self.fig.canvas.flush_events();
plt.show();
if self.stepmode:
a = input("Press Enter to step or c to continue:")
if a == 'c':
self.stepmode = False
print("finishing...")
else:
time.sleep(self.timer);
## -------- ##
mid = int( (start+end)/2 );
if (cutdim == 0):
n.lessChild = self.partition(points, start, mid, cutdim+1, minx, miny, e[0], maxy);
n.greaterChild = self.partition(points, mid, end, cutdim+1, s[0], miny, maxx, maxy);
elif (cutdim == 1):
n.lessChild = self.partition(points, start, mid, cutdim+1, minx, miny, maxx, e[1]);
n.greaterChild = self.partition(points, mid, end, cutdim+1, minx, s[1], maxx, maxy);
return n;
