some new features

.venv/lib/python3.12/site-packages/statsmodels/sandbox/regression/example_kernridge.py

@@ -0,0 +1,32 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from .kernridgeregress_class import GaussProcess, kernel_euclid
+
+
+m,k = 50,4
+upper = 6
+scale = 10
+xs = np.linspace(1,upper,m)[:,np.newaxis]
+#xs1 = xs1a*np.ones((1,4)) + 1/(1.0+np.exp(np.random.randn(m,k)))
+#xs1 /= np.std(xs1[::k,:],0)   # normalize scale, could use cov to normalize
+##y1true = np.sum(np.sin(xs1)+np.sqrt(xs1),1)[:,np.newaxis]
+xs1 = np.sin(xs)#[:,np.newaxis]
+y1true = np.sum(xs1 + 0.01*np.sqrt(np.abs(xs1)),1)[:,np.newaxis]
+y1 = y1true + 0.10 * np.random.randn(m,1)
+
+stride = 3 #use only some points as trainig points e.g 2 means every 2nd
+xstrain = xs1[::stride,:]
+ystrain = y1[::stride,:]
+xstrain = np.r_[xs1[:m/2,:], xs1[m/2+10:,:]]
+ystrain = np.r_[y1[:m/2,:], y1[m/2+10:,:]]
+index = np.hstack((np.arange(m/2), np.arange(m/2+10,m)))
+gp1 = GaussProcess(xstrain, ystrain, kernel=kernel_euclid,
+                   ridgecoeff=5*1e-4)
+yhatr1 = gp1.predict(xs1)
+plt.figure()
+plt.plot(y1true, y1,'bo',y1true, yhatr1,'r.')
+plt.title('euclid kernel: true y versus noisy y and estimated y')
+plt.figure()
+plt.plot(index,ystrain.ravel(),'bo-',y1true,'go-',yhatr1,'r.-')
+plt.title('euclid kernel: true (green), noisy (blue) and estimated (red) '+
+          'observations')