#if deceased population is higher then 1.000.000 people, classify as 1
classes[result[:, -1] > 1000000] = 1
return classes
#load data
results = load_results(r'../analysis/1000 flu cases.cPickle')
experiments, results = results
#extract results for 1 policy
logicalIndex = experiments['policy'] == 'no policy'
newExperiments = experiments[ logicalIndex ]
newResults = {}
for key, value in results.items():
newResults[key] = value[logicalIndex]
results = (newExperiments, newResults)
#perform prim on modified results tuple
boxes = prim.perform_prim(results, classify,
threshold=0.8,
threshold_type=1,
pasting=True)
#print prim to std_out
prim.write_prim_to_stdout(boxes)
#visualize
prim.show_boxes_individually(boxes, results)
prim.show_boxes_together(boxes, results)
plt.show()
from expWorkbench import load_results
from analysis.prim import perform_prim, write_prim_to_stdout
from analysis.prim import show_boxes_individually
def classify(data):
result = data["total fraction new technologies"]
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 0.8] = 1
return classes
if __name__ == "__main__":
results = load_results(r"CESUN_optimized_1000_new.cPickle")
experiments, results = results
logicalIndex = experiments["policy"] == "Optimized Adaptive Policy"
newExperiments = experiments[logicalIndex]
newResults = {}
for key, value in results.items():
newResults[key] = value[logicalIndex]
results = (newExperiments, newResults)
boxes = perform_prim(results, "total fraction new technologies", threshold=0.6, threshold_type=-1)
write_prim_to_stdout(boxes)
show_boxes_individually(boxes, results)
plt.show()
def classify(data):
result = data['total fraction new technologies']
classes = np.zeros(result.shape[0])
classes[result[:, -1] > 0.8] = 1
return classes
if __name__ == '__main__':
results = load_results(r'CESUN_optimized_1000_new.cPickle')
experiments, results = results
logicalIndex = experiments['policy'] == 'Optimized Adaptive Policy'
newExperiments = experiments[logicalIndex]
newResults = {}
for key, value in results.items():
newResults[key] = value[logicalIndex]
results = (newExperiments, newResults)
boxes = perform_prim(results,
'total fraction new technologies',
threshold=0.6,
threshold_type=-1)
write_prim_to_stdout(boxes)
show_boxes_individually(boxes, results)
plt.show()
