<p>发生这种差异的原因是<a href="https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html" rel="nofollow noreferrer">polyfit()</a>的<code>rcond</code>隐藏参数对于float32和float64是不同的。这是近似的相对误差。对于float32,其默认值约为2e-7,对于float64,其默认值约为2e-16。如果您自己指定相同的rcond参数,那么您将得到相同的结果</p>
<p>下面的代码使用<code>rcond</code>参数,还使用<code>np.polyval</code>绘制绘图,以显示几乎相同的视觉结果</p>
<p><a href="https://replit.com/@moytrage/StackOverflow66969023#main.py" rel="nofollow noreferrer">Try it online!</a></p>
<pre><code>import numpy as np
from numpy.polynomial import polynomial
import matplotlib.pyplot as plt
x32 = np.array([
1892.8972, 1893.1168, 1893.1626, 1893.4313, 1893.4929, 1895.6392,
1895.7642, 1896.4286, 1896.5693, 1897.313, 1898.4648
], dtype = 'f4')
y32 = np.array([
510.83655, 489.91592, 486.4508, 469.21814, 465.7902, 388.65576,
385.37637, 369.07236, 365.8301, 349.7118, 327.4062
], dtype = 'f4')
x64 = x32.astype('f8')
y64 = y32.astype('f8')
rcond = 2e-7
a, residuals1, _, _, _ = np.polyfit(x32, y32, 2, full=True, rcond = rcond)
b, residuals2, _, _, _ = np.polyfit(x64, y64, 2, full=True, rcond = rcond)
c, (residuals3, _, _, _) = polynomial.polyfit(x32, y32, 2, full=True, rcond = rcond)
d, (residuals4, _, _, _) = polynomial.polyfit(x64, y64, 2, full=True, rcond = rcond)
print(residuals1, residuals2, residuals3, residuals4)
# [] [195.86309188] [] [195.86309157]
print(a) # [ 3.54575804e+00 -1.34738721e+04 1.28004924e+07]
print(b) # [-8.70836523e-03 7.50419309e-02 3.15525483e+04]
print(c) # [ 1.28004924e+07 -1.34738721e+04 3.54575804e+00]
print(d) # [ 3.1552398e+04 7.5099051e-02 -8.7083541e-03]
plt.plot(x64, y64, label = 'orig')
plt.plot(x32, np.polyval(a, x32), label = 'x32_v0')
plt.plot(x64, np.polyval(b, x64), label = 'x64_v0')
plt.plot(x32, np.polyval(c[::-1], x32), label = 'x32_v1')
plt.plot(x64, np.polyval(d[::-1], x64), label = 'x64_v1')
plt.legend()
plt.show()
</code></pre>
<p><a href="https://i.stack.imgur.com/YNG3s.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/YNG3s.png" alt="enter image description here"/></a></p>