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colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo

lldb -f arke_base/install/arke_base/lib/arke_base/arke_hardware_interface_node -- --ros-args --params-file arke_base/config/arke_diff_drive_controller.yaml

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title: Kitti ROSBAG 2011-09-26_drive_0002_synced

shortname: kitti

description: >
             Kitti LIDAR, camera, IMU and GPS data.

references:
  - http://www.cvlibs.net/datasets/kitti/raw_data.php

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from launch import LaunchDescription, LaunchIntrospector, LaunchService
from launch_ros import actions, get_default_launch_description


def generate_launch_description():
    node = actions.Node(
        package='', node_executable='', output='screen')
    return LaunchDescription([plan_route])

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#! /bin/bash

# get path of current script: https://stackoverflow.com/a/39340259/207661
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
pushd "$SCRIPT_DIR"  >/dev/null

set -e
# set -x

#check for correct verion of llvm

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sigma_f = 0.9 + 0.9 * ( 0.8*0.9 + 0.6*-0.3)

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error = 1 - np.dot(list(source_trustworthiness.values()), list(source_trustworthiness_old.values()) / (
        np.linalg.norm(list(source_trustworthiness.values())) * np.linalg.norm(
    list(source_trustworthiness_old.values()))))

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def compute_source_trust(data, sources):
    '''
    Compute every source trustworthiness. The trustworthiness score is the average confidence of
    all facts supplied by source w
    :param data: Dataframe all facts for object O
    :param sources: dict all unique sources and current scores
    :return: dict of unique sources with updated scores
    '''
    for source in sources:
            # t(w) trustworthiness of website w

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def compute_final_confidence(data, confidence):
    for idx, claim in data.iterrows():
        data.at[idx, 'confidence'] = 1 / (1 + np.exp(-DAMPING_FACTOR * claim['confidence']))
    return (data, confidence)

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import jellyfish
def implicates(fact, fact_sources):
    '''
    How many sources implicates this fact
    :param fact: dataframe row
    :param fact_sources: dataframe
    :return:
    '''
    return [jellyfish.jaro_winkler(fact.lower(),f.lower()) - 0.5 for f in fact_sources]

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def compute_confidence_score_with_similarity(data, confidence, attribute_key):
    '''
    Compute the confidence score of a claim based on its already computed confidence score
    and the similarity between it and the other claims.
    Then set the confidence value to this new confidence computed with similarity measure.
    '''
    # get unique facts for object
    facts_set = data[attribute_key].unique()
    # create fact : confidence dict
    facts_confidence = {x[attribute_key]: x['confidence'] for _, x in data.iterrows()}