I have a set of data ([x[0],x[1]],y), many points in 3D space
and use scikit-learn to fit a learn model.
How I can calculate the distance between all the points to the fitting plane?
Does sklearn provide such function? I mean perpendicular distance. My code works but too manually.
I am looking for an existing quick function in a package like sklearn. Thanks.
def Linfit3D(x,y):
# x is a 2D array, they should be location of each bump, x_loc and y_loc
# y is the CTV or BTV that need to be fit to the least square plane
# three value will be returned, a,b, and c, which indicate a + b*x1 + c*x2 =y
model = sklearn.linear_model.LinearRegression()
model.fit(x, y)
coefs = model.coef_
intercept = model.intercept_
print("Equation: y = {:.5f} + {:.5f}*x1 + {:.5f}*x2".format(intercept, coefs[0],coefs[1]))
a=coefs[0]
b=coefs[1]
c=-1
d=intercept
return a,b,c,d
def point_to_plane_dist(x,y, a, b, c, d):
# the plane equation is: a*x + b*y + c*z + d = 0, and typically c=-1
# so the plane equation typicall is z = a*x + b*y + d
# and output has concerned the positive/negtive of point on top/bottom of the plane
f = abs((a * x[0] + b * x[1] + c * y + d))
e = (math.sqrt(a * a + b * b + c * c))
zp=a*x[0]+b*x[1]+d
# print('y = %2f, zp = %2f' %(y,zp))
if y>=zp:
return f/e
elif y<zp:
return (f/e)*(-1)
