Python distributions.Poisson() Examples

The following are 9 code examples of distributions.Poisson(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module distributions , or try the search function .
Example #1
Source File: lfads.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #2
Source File: lfads.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #3
Source File: lfads.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #4
Source File: lfads.py    From hands-detection with MIT License 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #5
Source File: lfads.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #6
Source File: lfads.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #7
Source File: lfads.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #8
Source File: lfads.py    From models with Apache License 2.0 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd
Example #9
Source File: lfads.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def spikify_rates(rates_bxtxd):
    """Randomly spikify underlying rates according a Poisson distribution

    Args:
      rates_bxtxd: a numpy tensor with shape:

    Returns:
      A numpy array with the same shape as rates_bxtxd, but with the event
      counts.
    """

    B,T,N = rates_bxtxd.shape
    assert all([B > 0, N > 0]), "problems"

    # Because the rates are changing, there is nesting
    spikes_bxtxd = np.zeros([B,T,N], dtype=np.int32)
    for b in range(B):
      for t in range(T):
        for n in range(N):
          rate = rates_bxtxd[b,t,n]
          count = np.random.poisson(rate)
          spikes_bxtxd[b,t,n] = count

    return spikes_bxtxd