You can also use numpy's polyfit:
data = np.array[[[1,5], [2,10], [3,15], [4,20], [5,25]]]
fit = np.polyfit[data[:,0], data[:,1] ,1] #The use of 1 signifies a linear fit.
fit
[ 5.00000000e+00 1.58882186e-15] #y = 5x + 0
line = np.poly1d[fit]
new_points = np.arange[5]+6
new_points
[ 6, 7, 8, 9, 10]
line[new_points]
[ 30. 35. 40. 45. 50.]
This allows you to alter the degree of the polynomial fit quite easily as the function polyfit take thes following arguments np.polyfit[x data, y data, degree]. Shown is a linear fit where the returned array looks like fit[0]*x^n + fit[1]*x^[n-1] + ... + fit[n-1]*x^0 for any degree n. The poly1d function allows you turn this array into a function that returns the
value of the polynomial at any given value x.
In general extrapolation without a well understood model will have sporadic results at best.
Exponential curve fitting.
from scipy.optimize import curve_fit
def func[x, a, b, c]:
return a * np.exp[-b * x] + c
x = np.linspace[0,4,5]
y = func[x, 2.5, 1.3, 0.5]
yn = y + 0.2*np.random.normal[size=len[x]]
fit ,cov = curve_fit[func, x, yn]
fit
[ 2.67217435 1.21470107 0.52942728] #Variables
y
[ 3. 1.18132948 0.68568395 0.55060478 0.51379141] #Original data
func[x,*fit]
[ 3.20160163 1.32252521 0.76481773 0.59929086 0.5501627 ] #Fit to original + noise
Tôi đã nghĩ ra một giải pháp khá thanh lịch [IMHO], vì vậy tôi không thể cưỡng lại việc đăng nó:
from bisect import bisect_left
class Interpolate[object]:
def __init__[self, x_list, y_list]:
if any[y - x