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Yueningwang committed Jan 29, 2019
1 parent e080714 commit f52d8d1
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2 changes: 1 addition & 1 deletion model_create/latest.json
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@@ -1 +1 @@
{"layer_number": 1, "starting_n": 72, "best_R": 0.9620589137849329, "best_param": [["tanh", 10], ["tanh", 18], "linear"], "cumulative_time": 4060.8181924819946}
{"layer_number": 2, "starting_n": 90, "best_R": 0.9396900626947863, "best_param": [["tanh", 18], ["tanh", 5], ["linear", 10], "linear"], "cumulative_time": 7373.343111991882}
6 changes: 5 additions & 1 deletion model_create/model_create.py
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Expand Up @@ -268,7 +268,6 @@ def model_continue_search(data,test_fraction,random_state,cumulative_time,params
print(x)
pr.check_write(x,'latest.json')
print("")
f.close()

print(best_param)
print(best_R)
Expand Down Expand Up @@ -332,6 +331,11 @@ def continue_layer_search(data,test_fraction,random_state,cumulative_time,params
print("best R for the combination %s with %d hidden layer is %0.4f" % (best_param,layer,best_R))
best_list.append(best_param)
best_R_list.append(best_R)
else:
best_param,best_R = model_initial_search(data,test_fraction,random_state,layer,cumulative_time,params)
print("best R for the combination %s with %d hidden layer is %0.4f" % (best_param,layer,best_R))
best_list.append(best_param)
best_R_list.append(best_R)
print(best_list,best_R_list)
max_R = best_R_list[0]
max_param = best_list[0]
Expand Down
11 changes: 7 additions & 4 deletions model_create/parameter.py
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Expand Up @@ -21,9 +21,12 @@ def pca_eval(data):
It takes formatted data as parameter
The explained variance ratio is printed
'''
x = data['X_var']
x = StandardScaler().fit_transform(x)
X_var,Y_var,input_dim,output_dim = dp.data_info(data)
x = StandardScaler().fit_transform(X_var)
pca = PCA()
pca.fit(x)
print(pca.explained_variance_ratio_)

print(pca.explained_variance_ratio_)
return pca.explained_variance_ratio_



91 changes: 91 additions & 0 deletions model_create/results2.txt

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