alexklibisz

def weighted_log_loss(yt, yp):
    '''Log loss that weights false positives or false negatives more.
    Punish the false negatives if you care about making sure all the neurons
    are found and don't mind some false positives. Vice versa for punishing
    the false positives. Concept taken from the UNet paper where they
    weighted boundary errors to get cleaner boundaries.'''

    emphasis = 'fn'
    assert emphasis in ['fn', 'fp']
    m = 2

thatguysimon

# A python script to turn annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models
# - NER format based on: http://nlp.stanford.edu/software/crf-faq.html#a
# - RE format based on: http://nlp.stanford.edu/software/relationExtractor.html#training

# Usage:
# 1) Install the pycorenlp package
# 2) Run CoreNLP server (change CORENLP_SERVER_ADDRESS if needed)
# 3) Place .ann and .txt files from brat in the location specified in DATA_DIRECTORY
# 4) Run this script

shiona

pcm.frontroute {
    type route
    slave.pcm "dmix:USB"
    ttable.0.0 1
    ttable.1.1 1
}
pcm.frontusb {
    type plug
    slave.pcm "frontroute"
    hint {

sebleier

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