update notebook

python-module/ML_of_Forces/GP_custom_gen.py

@@ -402,7 +402,7 @@ def cov_sim_tries(a, b, sig): # SO(3) linear covariant kernel
     ri, rj = np.sqrt(ris), np.sqrt(rjs)
     
     # take only particles within the first shell
-    cut = 4.17
+    cut = 4.5
     a, b = a[ri<= cut], b[rj <= cut]
     ris, rjs = ris[ri<= cut], rjs[rj <= cut]
     ri, rj = ri[ri<= cut], rj[rj <= cut]

python-module/main.py

 from ML_of_Forces.DataModel import ForceConfs
 from ML_of_Forces.GP_custom_gen import GaussianProcess3 as GPvec
+import numpy as np
+
+from ML_of_Forces.ManyBodyExpansion import MBExp
 
 
 data = ForceConfs()
 data.read_json_data(filename='../data-mlforce/Si_TB_300K.json')
 
-train_confs, train_force, test_confs, test_force = data.subsampling(30, 10, random=True)
+train_confs, train_force, test_confs, test_force = data.subsampling(40, 10, random=True)
+
+gp = GPvec(
+    ker=['id'],
+    fvecs=['cov_sim'],
+    nugget=1e-8,
+    theta0=np.array([None]),
+    sig=.5,
+    bounds=((0.1, 10.),),
+    optimizer=None,
+    calc_error=True,
+    eval_grad=False,
+)
+
+
+gp.fit(train_confs, train_force)
+pred_force, var_force = gp.predict(test_confs)
+
+# print(pred_force)
+
+# print(test_force)
+
+# error = np.sqrt(np.sum((pred_force - test_force)**2, axis=1))
+
+# print(error.shape)
+
+# print(var_force)
+
+
+
+
+
+# print(train_confs)
+
+fit = MBExp(gp)
 
+fit.PW_S_fit(0.1,4)
 
-print(train_confs)numpy
\ No newline at end of file
+fit.pair_potential_scalars(0.1, 4, Delta_d=0.1)