def argmin(c, lo, hi):
cut = None
xl, xr = Num([]), Num([])
yl, yr = Num([]), Num([])
for i in range(lo, hi + 1):
xr.num_inc(rows[i][c])
yr.num_inc(rows[i][goal])
bestx = xr.sd
besty = yr.sd
mu = yr.mu
n = yr.n
if (hi - lo > 2 * enough):
for i in range(lo, hi + 1):
x = rows[i][c]
y = rows[i][goal]
xl.num_inc(x)
xr.num_dec(x)
yl.num_inc(y)
yr.num_dec(y)
if xl.n >= enough and xr.n >= enough:
tmpx = Num.num_xpect(data, xl, xr) * param_margin
tmpy = Num.num_xpect(data, yl, yr) * param_margin
if tmpx < bestx and tmpy < besty:
cut, bestx, besty = i, tmpx, tmpy
return cut, mu, n, besty
def argmin(col, lo, hi):
cut = None
if hi - lo > 2 * enough:
l, r = Num(), Num()
for i in range(lo, hi + 1):
r.num_inc(sorted_rows[i][col])
best = r.standard_deviation
for i in range(lo, hi + 1):
curr_val = sorted_rows[i][col]
l.num_inc(curr_val)
r.num_dec(curr_val)
if l.count >= enough and r.count >= enough:
tmp = l.num_xpect(r) * 1.05
if tmp < best:
cut, best = i, tmp
return cut
def argmin(col, lo, hi):
cut = None
xl, xr = Num(), Num()
yl, yr = Num(), Num()
for i in range(lo, hi + 1):
xr.num_inc(self.data_rows[i][col])
yr.num_inc(self.data_rows[i][goal])
bestx = xr.standard_deviation
besty = yr.standard_deviation
mu = yr.mean
if (hi - lo) > 2 * enough:
for i in range(lo, hi + 1):
x = self.data_rows[i][col]
y = self.data_rows[i][goal]
xr.num_dec(x)
xl.num_inc(x)
yr.num_dec(y)
yl.num_inc(y)
if xl.count >= enough and xr.count >= enough:
tmpx = xl.num_xpect(xr) * 1.05
tmpy = yl.num_xpect(yr) * 1.05
if tmpx < bestx and tmpy < besty:
cut, bestx, besty = i, tmpx, tmpy
return cut, mu, besty
def argmin(c, lo, hi):
cut = None
if (hi-lo > 2*enough):
l = Num([])
r = Num([])
for i in range(lo,hi+1):
r.num_inc(rows[i][c])
best = r.sd
for i in range(lo,hi+1):
x = rows[i][c]
l.num_inc(x)
r.num_dec(x)
if l.n >= enough and r.n >= enough:
tmp = Num.num_xpect(data, l, r) * param_margin
if tmp < best:
cut, best = i, tmp
return cut