Andreas Klintberg klintan

## angular-calendar-module.js
'use strict';

/* Google Calendar API Module */

angular.module('myApp.calendar', []).
    value('resp', {
        nullMethod: function() {
            return 'value';
        },
        handleClientLoad: function() {

## keras_regression.py
import numpy as np
import pandas as pd
from math import sqrt

from keras.models import Sequential
from keras.layers.core import Dense, Activation

from sklearn.feature_selection import VarianceThreshold
from sklearn.preprocessing import StandardScaler

## ff_data_prep.py
#data preparation

import pandas as pd
import numpy as np

from os import listdir
from os.path import isfile, join

#training file folder (All your csv data files)
file_path = 'CAX_Train'

## RandomForestRegressor.py
import pandas as pd
import numpy as np

from math import sqrt

from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor

from sklearn.preprocessing import StandardScaler

## effecient_nested_for.py
import pprint
import collections

H1 =  [1, 2, 3, 4, 5]
H2 =  [1, 2, 3, 4, 5]
all_dist = []

#http://stackoverflow.com/questions/635483/what-is-the-best-way-to-implement-nested-dictionaries-in-python?noredirect=1&lq=1
class AutoVivification(dict):
    """Implementation of perl's autovivification feature."""

## load_sem3d_data.ipynb

      
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                klintan
                / load_sem3d_data.ipynb
            
            
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              January 31, 2018 07:28
            
              
                Load Semantic3D data
              
          
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## initialization.py
max_iteration_count = 10
it = 0
error = 99
tol = 0.001
# intialize source trustworthiness structure
source_trustworthiness = {k: -np.log(1 - 0.9) for k in data['source'].unique()}

## iteration.py
while error > tol and it < max_iteration_count:
    source_trustworthiness_old = copy.deepcopy(source_trustworthiness)

    # 1. Compute fact confidence score
    data = compute_confidence(data, objects, source_trustworthiness,   attribute_key)

    # 2. Compute source trustworthiness score
    source_trustworthiness = compute_source_trust(data, source_trustworthiness)

    # Check convergence of the process

## compute_confidence.py
def compute_confidence(df, objects, source_trust, attribute_key):
    # compute claims confidence score

    all_objects_data = pd.DataFrame()
    for obj in objects:
        data = df[df['object'] == obj]

       # Sub-step 1. compute from source trust
       data, confidence = compute_confidence_score(data, source_trust, attribute_key)

## compute_confidence_score.py
def compute_confidence_score(data, source_trust, attribute_key):
    '''
    compute the confidence score using the sources trust worthiness, sum the scores for all sources
    for a specific claim.
    '''
    # loop through each source for a book
    for idx, claim in data.iterrows():
        # get all sources for a specific claim
        sources = get_sources_for_claim(data, claim, attribute_key)
        # sum the trust score for all the sources for a specific claim
	'use strict';

	/* Google Calendar API Module */

	angular.module('myApp.calendar', []).
	value('resp', {
	nullMethod: function() {
	return 'value';
	},
	handleClientLoad: function() {
	import numpy as np
	import pandas as pd
	from math import sqrt

	from keras.models import Sequential
	from keras.layers.core import Dense, Activation

	from sklearn.feature_selection import VarianceThreshold
	from sklearn.preprocessing import StandardScaler
	#data preparation

	import pandas as pd
	import numpy as np

	from os import listdir
	from os.path import isfile, join

	#training file folder (All your csv data files)
	file_path = 'CAX_Train'
	import pprint
	import collections

	H1 = [1, 2, 3, 4, 5]
	H2 = [1, 2, 3, 4, 5]
	all_dist = []

	#http://stackoverflow.com/questions/635483/what-is-the-best-way-to-implement-nested-dictionaries-in-python?noredirect=1&lq=1
	class AutoVivification(dict):
	"""Implementation of perl's autovivification feature."""
	max_iteration_count = 10
	it = 0
	error = 99
	tol = 0.001
	# intialize source trustworthiness structure
	source_trustworthiness = {k: -np.log(1 - 0.9) for k in data['source'].unique()}
	while error > tol and it < max_iteration_count:
	source_trustworthiness_old = copy.deepcopy(source_trustworthiness)

	# 1. Compute fact confidence score
	data = compute_confidence(data, objects, source_trustworthiness, attribute_key)

	# 2. Compute source trustworthiness score
	source_trustworthiness = compute_source_trust(data, source_trustworthiness)

	# Check convergence of the process
	def compute_confidence(df, objects, source_trust, attribute_key):
	# compute claims confidence score

	all_objects_data = pd.DataFrame()
	for obj in objects:
	data = df[df['object'] == obj]

	# Sub-step 1. compute from source trust
	data, confidence = compute_confidence_score(data, source_trust, attribute_key)
	def compute_confidence_score(data, source_trust, attribute_key):
	'''
	compute the confidence score using the sources trust worthiness, sum the scores for all sources
	for a specific claim.
	'''
	# loop through each source for a book
	for idx, claim in data.iterrows():
	# get all sources for a specific claim
	sources = get_sources_for_claim(data, claim, attribute_key)
	# sum the trust score for all the sources for a specific claim