arendu-zz

import numpy as np
from sklearn.metrics import roc_auc_score
actual = [1, 0, 0, 1, 1, 0, 0, 1, 0, 1]
predicted = [0.8, 0.2, 0.6, 0.3, 0.1, 0.2, 0.3, 0.9, 0.2, 0.7]
y_true = np.array(actual)
y_pred = np.array(predicted)
heaviside_new = np.vectorize(lambda x : 0.0 if x<0 else (.5 if x == 0 else 1.0))
heaviside = np.vectorize(lambda x : 0 if x<0 else .5 if x == 0 else 1)  #buggy

def Broadcast_AUC(y_true, y_pred):

arendu-zz

#!/usr/bin/env python
__author__ = 'arenduchintala'
import theano
import theano.tensor as T
import numpy as np
a = T.vector('a')
M = T.matrix('M')
func = theano.function(inputs=[a,M], outputs=T.dot(M,a))
a_np = np.float32(np.random.rand(10,))
M_np = np.float32(np.random.rand(10,10))

arendu-zz

from bokeh.plotting import figure, ColumnDataSource
from bokeh.models import HoverTool


def scatter_with_hover(df, x, y,
                       fig=None, cols=None, name=None, marker='x',
                       fig_width=500, fig_height=500, **kwargs):
    """
    Plots an interactive scatter plot of `x` vs `y` using bokeh, with automatic
    tooltips showing columns from `df`.

arendu-zz

from bokeh.plotting import figure, ColumnDataSource
from bokeh.models import HoverTool


def scatter_with_hover(df, x, y,
                       fig=None, cols=None, name=None, marker='x',
                       fig_width=500, fig_height=500, **kwargs):
    """
    Plots an interactive scatter plot of `x` vs `y` using bokeh, with automatic
    tooltips showing columns from `df`.

arendu-zz

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams
from lasagne.updates import adam
from lasagne.utils import collect_shared_vars

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing

arendu-zz

set nocp
syntax on
set modeline
set ls=2
set backspace=2
set clipboard=unnamed
set number
set encoding=utf-8 
"vijays recommendation
" set paste

arendu-zz

# File: .screenrc
# # Screen rc file – config options
#
# # don’t display copyright page
startup_message off
#
# # no audible bell, just visual
vbell off
# # detach on hangup
autodetach on

arendu-zz

[global]
mode = FAST_RUN
floatX = float32
int_division = floatX
exception_verbosity = high
warn_float64 = raise
allow_gc = False
compiledir_format=compiledir-%(platform)s-%(processor)s-%(python_version)s-%(python_bitwidth)s-%(hostname)s
linker = cvm
openmp = true

arendu-zz

__author__ = 'arenduchintala'
from edu.jhu.pacaya.gm.model import FactorGraph, FgModel, Var, VarSet, ExpFamFactor, ExplicitExpFamFactor, ExplicitFactor, VarConfig, ClampFactor
from edu.jhu.pacaya.gm.train import CrfTrainer
from edu.jhu.pacaya.gm.train.CrfTrainer import CrfTrainerPrm
from edu.jhu.pacaya.gm.data import FgExampleList, FgExampleDiskStore, FgExampleMemoryStore, LabeledFgExample
from edu.jhu.pacaya.gm.feat import FeatureVector
from edu.jhu.nlp.joint import OptimizerFactory
from edu.jhu.pacaya.gm.inf.BeliefPropagation import BeliefPropagationPrm
from edu.jhu.pacaya.gm.inf.BeliefPropagation import BpScheduleType
from edu.jhu.pacaya.gm.inf.BeliefPropagation import BpUpdateOrder

arendu-zz

#!/usr/bin/env python

# NOTE : This script assumes that the aligments are in the src-tgt format

import optparse
import pprint
import sys
import numpy as np

optparser = optparse.OptionParser()