```import numpy as np
from sklearn.base import BaseEstimator
from sklearn.metrics import r2_score
from sklearn.multioutput import MultiOutputRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import PolynomialFeatures

from sparsereg.model.base import equation
from sparsereg.model.base import STRidge
from sparsereg.preprocessing.symfeat import SymbolicFeatures

def _derivative(x, dt=1.0):
if isinstance(dt, (list, np.ndarray)):
if len(dt) < 3:
raise ValueError("dt has too few elements")
dx = np.zeros_like(x)
dx[1:-1, :] = (x[2:, :] - x[:-2, :]) / (dt[2:] - dt[:-2])

dx[0, :] = (x[1, :] - x[0, :]) / (dt[1] - dt[0])
dx[-1, :] = (x[-1, :] - x[-2, :]) / (dt[-1] - dt[-2])
else:
dx = np.zeros_like(x)
dx[1:-1, :] = (x[2:, :] - x[:-2, :]) / (2.0 * dt)

dx[0, :] = (x[1, :] - x[0, :]) / dt
dx[-1, :] = (x[-1, :] - x[-2, :]) / dt

return dx

class SINDy(BaseEstimator):
def __init__(
self,
alpha=1.0,
threshold=0.1,
degree=3,
operators=None,
dt=1.0,
n_jobs=1,
derivative=None,
feature_names=None,
kw={},
):
self.alpha = alpha
self.threshold = threshold
self.degree = degree
self.operators = operators
self.n_jobs = n_jobs
self.derivative = derivative or FunctionTransformer(func=_derivative, kw_args={"dt": dt})
self.feature_names = feature_names
self.kw = kw

def fit(self, x, y=None):
if y is not None:
xdot = y
else:
xdot = self.derivative.transform(x)

if self.operators is not None:
feature_transformer = SymbolicFeatures(
exponents=np.linspace(1, self.degree, self.degree), operators=self.operators
)
else:
feature_transformer = PolynomialFeatures(degree=self.degree, include_bias=False)

steps = [
("features", feature_transformer),
("model", STRidge(alpha=self.alpha, threshold=self.threshold, **self.kw)),
]
self.model = MultiOutputRegressor(Pipeline(steps), n_jobs=self.n_jobs)
self.model.fit(x, xdot)

self.n_input_features_ = self.model.estimators_[0].steps[0][1].n_input_features_
self.n_output_features_ = self.model.estimators_[0].steps[0][1].n_output_features_
return self

def predict(self, x):
return self.model.predict(x)

def equations(self, precision=3):
names = self.model.estimators_[0].steps[0][1].get_feature_names(input_features=self.feature_names)
return [equation(est, names, precision=precision) for est in self.model.estimators_]

def score(self, x, y=None, multioutput="uniform_average"):
if y is not None:
xdot = y
else:
xdot = self.derivative.transform(x)
return r2_score(self.model.predict(x), xdot, multioutput=multioutput)

@property
def complexity(self):
return sum(est.steps[1][1].complexity for est in self.model.estimators_)
```