Example #1
0
              # Eigen-Adaptive
              'F_min' : 0.9,
              'epsilon' : 0.05,
              # Pedro Adaptive
              'e_low' : 0.95,
              'e_high' : 0.98,
              'static_r' : 0,
              'r_upper_bound' : None,
              'fix_init_Q' : 0,
              'small_value' : 0.0001,
              'ignoreUp2' : 0 }

    p['x_thresh'] = sp.stats.t.isf(0.5* p['FP_rate'], p['sample_N'])

    ''' Load Data '''
    data = load_ts_data('isp_routers', 'full')
    #data, sins = sin_rand_combo(5, 1000, [10, 35, 60], noise_scale = 0.2, seed = 1)
    data = zscore(data)
    z_iter = iter(data)
    numStreams = data.shape[1]

    '''Initialise'''
    st = initialise(p, numStreams)
    
    '''Begin Frahst'''
    # Main iterative loop. 
    for zt in z_iter:

        zt = zt.reshape(zt.shape[0],1)   # Convert to a column Vector 
        st['anomaly'] = False
        '''Frahst Version '''
Example #2
0
        # Eigen-Adaptive
        'F_min': 0.9,
        'epsilon': 0.05,
        # Pedro Adaptive
        'e_low': 0.95,
        'e_high': 0.98,
        'static_r': 0,
        'r_upper_bound': None,
        'fix_init_Q': 0,
        'small_value': 0.0001,
        'ignoreUp2': 0
    }

    p['x_thresh'] = sp.stats.t.isf(0.5 * p['FP_rate'], p['sample_N'])
    ''' Load Data '''
    data = load_ts_data('isp_routers', 'full')
    #data, sins = sin_rand_combo(5, 1000, [10, 35, 60], noise_scale = 0.2, seed = 1)
    data = zscore(data)
    z_iter = iter(data)
    numStreams = data.shape[1]
    '''Initialise'''
    st = initialise(p, numStreams)
    '''Begin Frahst'''
    # Main iterative loop.
    for zt in z_iter:

        zt = zt.reshape(zt.shape[0], 1)  # Convert to a column Vector
        st['anomaly'] = False
        '''Frahst Version '''
        st = FRAHST_V7_0_iter(zt, st, p)
        # Calculate reconstructed data if needed
        "M": 5,
        "pA": 0.1,
        "noise_sig": 0.1,
        "seed": None,
    }

    anomaly_type = "peak_dip"

    gen_funcs = dict(
        peak_dip=gen_a_peak_dip, grad_persist=gen_a_grad_persist, step=gen_a_step, trend=gen_a_periodic_shift
    )

    # D = gen_funcs[anomaly_type](**a)
    # data = D['data']

    data = load_ts_data("isp_routers", "full")
    # execfile('/Users/chris/Dropbox/Work/MacSpyder/Utils/gen_simple_peakORshift_data.py')
    # data = B

    """ Mean Centering """
    # data = zscore_win(data, 100)
    data = zscore(data)
    z_iter = iter(data)
    numStreams = data.shape[1]

    """Initialise"""
    Frahst_alg = FRAHST("F-7.A-recS.R-static", p, numStreams)

    """Begin Frahst"""
    # Main iterative loop.
    for zt in z_iter:
Example #4
0
                   step = gen_a_step)
  
  
  """ Choice of data sets. Comment/Uncomment to choose. """
  """----------------------------------------------------"""
  
  ''' Synthetic Data sets ''' 
  #data_name = 'synth'
  #D = gen_funcs[anomaly_type](**a)  
  #raw_data = D['data']
  #data = raw_data.copy()
  
  '''ISP data sets '''

  data_name = 'isp_routers'
  raw_data = load_ts_data(data_name, 'full')
  data = raw_data.copy()
  
  ''' Sensor Motes data sets '''
  #data_name = 'motes_l'
  #raw_data = load_data(data_name)
  #data = clean_zeros(raw_data, cpy=1)  
  
  
  ''' Data Preprocessing '''
  """ Data is loaded into memory, mean centered and standardised
  then converted to an iterable to read by the CD-ST each iteration"""
  
  #data = zscore_win(data, 100) # Sliding window implimentation
  data = zscore(data) # Batch method implimentation