print "maximum is at x=", np.where(ysum==max) ##fit gaussian #fit only part of my data in the chosen range [glo:ghi] x=wavelen_pix[glo:ghi] y=ysum[glo:ghi] def func(x, a, x0, sigma): return a*np.exp(-(x-x0)**2/float((2*sigma**2))) sig=np.std(ysum[500:100...
from sklearn.gaussian_process.kernels import ConstantKernel, RBF # fit GPR kernel = ConstantKernel(constant_value=0.2, constant_value_bounds=(1e-4, 1e4)) * RBF(length_scale=0.5, length_scale_bounds=(1e-4, 1e4)) gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=2) gpr...
This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset is formed by 100 points loosely spaced following a noisy sine curve. There is therefore no ground truth value for the number of Gaussian co...
Obviously, we have to understand a single Gaussian distribution before understanding a Gaussian mixture. Suppose we have a sequence of data points and each data point only has one feature (1-Ddataset). We can plot the density of these data points along the axis of that feature. For example,...
fit() print(model.summary()) 输出结果: Formula: GFR~time+age+gender+micro+macro+micro:time+macro:time+(1+time|patient) Family: gaussian Inference: parametric Number of observations: 1378 Groups: {'patient': 200.0} Log-likelihood: -4754.489 AIC: 9508.979 Random effects: Name Var Std ...
gnb=GaussianNB()gnb.fit(train_x,train_y)# 对训练数据进行预测 predict_train=gnb.predict(train_x)print('Prediction on train data:',predict_train)# 在训练数据上的准确率 from sklearn.metricsimportaccuracy_score accuracy_train=accuracy_score(train_y,predict_train)print('Accuray score on train data...
n_jobs是指并发进程最大数量,设置为-1表示使用所有CPU核心进程。在Python3.4中,可以写一个Python的脚本,让fit()函数可以在main()函数里调用,也可以在Python自带命令行,IPython命令行和IPython Notebook运行。经过网格计算后的超参数在训练集中取得了很好的效果。
我们就可以写出如下的fit函数: @SVMClassdef fit(self, X, y, eval_train=False):# if more than two unique labels, call the multiclass versioniflen(np.unique(y)) >2:self.multiclass =Truereturnself.multi_fit(X, y, eval_train)# if labels given in...
mbk.fit(X) t_mini_batch = time.time() - t0 # Plot result fig = plt.figure(figsize=(8,3)) fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9) colors = ['#4EACC5','#FF9C34','#4E9A06'] # We want to have the...
) split = int(t*len(X)/100) reg.fit(X[:split],y[:split]) best_alpha = reg.best_params_['lasso__alpha'] best_iter= reg.best_params_['lasso__max_iter'] reg1= Lasso(alpha=best_alpha,max_iter=best_iter) X=imp.fit_transform(X,y) reg1.fit(X[:split]...