leverage_data.py

c=['#415952', '#f35134', '#243AB5']
def dataset_ (n=200, idx_outlier=0, ydistance=5):
    rng = np.random.RandomState(4)
    data  = np.dot(rng.rand(2, 2), rng.randn(2, n)).T
    data[idx_outlier:idx_outlier+1,1] = ydistance
    return data

N=200
inx=10
i = dataset_(N, inx)
carray=[c[0]]*N
carray=np.full(N,c[0])
carray[inx]='red'
fig, ax = plt.subplots(figsize=(8,6))
ax.scatter(i[:,0], i[:,1], c=carray, alpha=0.3)

leverage_implement.py

f_H = lambda i: np.dot(i, inv(np.dot(i.T, i))).dot(i.T)
H = f_H(i)
h=np.trace(H)/N # if h2* > 1 then not cutoff is applied

outliers=[]
for row in range(N):
    for col in range(N):
        if row == col:
            if H[row, col] > 2.*h:
                outliers.append(row)
     
carray=[c[0]]*N
carray = np.array(carray)
carray[outliers]='red'
fig, ax = plt.subplots(figsize=(8,6))
ax.scatter(i[:,0], i[:,1], c=carray, alpha=0.3)