scipy.optimize.least\u squares数据到哪里去了?

你的问题中的beta是什么?假设beta应该是包含b1,…,b3的向量,它只是一个约束优化问题,可以很容易地用 scipy's minimize 这样地:

import pandas as pd
import numpy as np
from scipy.optimize import minimize

# Your Data
df = pd.DataFrame(np.random.randint(low=0, high=10, size=(100,4)), columns=['Historic Rate', 'Overnight', '1M','3M'])
Y = np.array(df['Historic Rate'])
X = np.array(df[['Overnight','1M','3M']])

# Define the Model
model = lambda b, X: b[0] * X[:,0] + b[1] * X[:,1] + b[2] * X[:,2]

# The objective Function to minimize (least-squares regression)
obj = lambda b, Y, X: np.sum(np.abs(Y-model(b, X))**2)

# Bounds: b[0], b[1], b[2] >= 0
bnds = [(0, None), (0, None), (0, None)]

# Constraint: b[0] + b[1] + b[2] - 1 = 0
cons = [{"type": "eq", "fun": lambda b: b[0]+b[1]+b[2] - 1}]

# Initial guess for b[1], b[2], b[3]:
xinit = np.array([0, 0, 1])

res = minimize(obj, args=(Y, X), x0=xinit, bounds=bnds, constraints=cons)
print(f"b1={res.x[0]}, b2={res.x[1]}, b3={res.x[2]}")