def run_seism_experiment():
input_file = "eQnoise.csv"
generate_data.run(input_file)
print("time is :%f secs" % ((clk.time() - t0) / 60.))
model_params = swarm_over_data()
print("time is :%f secs" % ((clk.time() - t0) / 60.))
print(model_params)
if PLOT:
pass
#output = NuPICPlotOutput("sine3_output", show_anomaly_score=True)
else:
output = NuPICFileOutput("eQnoise_output", show_anomaly_score=True)
print("time is :%f min" % ((clk.time() - t0) / 60.))
model = ModelFactory.create(model_params)
model.enableInference({"predictedField": "acc"})
with open(input_file, "rb") as data_input:
csv_reader = csv.reader(data_input)
# skip header rows
csv_reader.next()
csv_reader.next()
csv_reader.next()
# the real data
for row in csv_reader:
time = float(row[0])
acc_value = float(row[1])
result = model.run({"acc": acc_value})
output.write(time, acc_value, result, prediction_step=PSTEPS)
output.close()
print("time is :%f min" % ((clk.time() - t0) / 60.))
def run(fname="verylargeseism_out"):
print("reading %s" % input_file)
print("using this model: ", model_params)
model = ModelFactory.create(model_params)
model.enableInference({"predictedField": "acc"})
print("created nupic model in :%f min" % ((clk.time() - t0) / 60.))
print("will output predictions and anomalies \n in this file: %s" % fname)
output = NuPICFileOutput(fname, show_anomaly_score=True)
with open(input_file, "rb") as data_input:
csv_reader = csv.reader(data_input)
# skip header rows
csv_reader.next()
csv_reader.next()
csv_reader.next()
# the real data
for row in csv_reader:
time = float(row[0])
acc_value = float(row[1])
result = model.run({"acc": acc_value})
output.write(time, acc_value, result, prediction_step=PSTEPS)
output.close()
print("time is :%f min" % ((clk.time() - t0) / 60.))
def predict():
from model_0 import model_params
input_file = "sine.csv"
model = ModelFactory.create(model_params.MODEL_PARAMS)
model.enableInference({"predictedField": "sine"})
output = NuPICFileOutput("sine_output", show_anomaly_score=True)
with open(input_file, "rb") as sine_input:
csv_reader = csv.reader(sine_input)
# skip header rows
csv_reader.next()
csv_reader.next()
csv_reader.next()
# the real data
for row in csv_reader:
angle = float(row[0])
sine_value = float(row[1])
result = model.run({"sine": sine_value})
output.write(angle, sine_value, result, prediction_step=1)
output.close()
for i in range(Data.shape[1]):
input_file = '/opt/share_code_data/' + str(Data.columns[i]) + '.csv'
with open(input_file, "rb") as sine_input:
csv_reader = csv.reader(sine_input)
# the real data
# skip header rows
csv_reader.next()
csv_reader.next()
csv_reader.next()
for row in csv_reader:
timeS = row[0]
value = float(row[6])
result = model.run({"value": value})
output.write(timeS, value, result, prediction_step=3)
#对leftD数据进行操作
maxl = 0
maxi = -1
for i in range(leftD.shape[1]): #找出最长的非0序列
if maxl < len(np.nonzero(leftD.iloc[:, i] == 0)):
maxl = len(np.nonzero(leftD.iloc[:, i] == 0))
maxi = i
if maxi != -1:
data = leftD.values[np.nonzero(leftD.values[:, maxi] != 0)[0],
maxi] #swarm只考虑非0数据
paras = swarm(data, number=maxi, col=leftD.columns[maxi]) #运行swarm
print('paras: ', paras) #best params
model = ModelFactory.create(paras)
model.enableInference({"predictedField": "value"})