aerinkim

const lineItemLabels = ['Item Amount', 'Description', 'Unit Price', 'Unit Quantity'];
const lintIssues = []
export function lint(response) {
  let all_record_annotations = [];
  if (response.annotations && response.records) {
    for (const record of response.records) {
        if(record.annotations) {
            for (const ann in record.annotations) {
                all_record_annotations.push(ann.ref);

aerinkim

const REGEX_contains_hashtag = /[#]/;

const labelsToCheck = ['Invoice Number'];


export function lint(response) {
  const lintIssues = [];
  if (response.annotations) {
    for (const annotation of response.annotations) {
      if (labelsToCheck.includes(annotation.label)) {

aerinkim

const REGEX_no_dollar_sign = /[!@#$%^&*()_+\-=\[\]{};':"\\|<>\/?~]/;

const labelsToCheck = ['Invoice Total', 'Invoice Amount Due', 'Item Amount', 'Unit Price'];


export function lint(response) {
  const lintIssues = [];
  if (response.annotations) {
    for (const annotation of response.annotations) {
      if (labelsToCheck.includes(annotation.label)) {

aerinkim

const REGEX_ISO_CURRENCY_CODE = /[A-Z]{3}/;

const labelsToCheck = ['Invoice Total', 'Invoice Amount Due', 'Item Amount', 'Unit Price'];


export function lint(response) {
  const lintIssues = [];
  if (response.annotations) {
    for (const annotation of response.annotations) {
      if (labelsToCheck.includes(annotation.label)) {

aerinkim

export function lint(response) {
  const lintIssues = [];
  let isLabelable = true;
  if (response.values) {
    for (const value of response.values) {
      if (value.field_id === 'invoice_labeling' && !value.selected.includes('yes')) {
        isLabelable = false;
      }
    }
    

aerinkim

aerinkim

class Calibration:
    """Calibration matrices and utils.

    3d XYZ are in 3D egovehicle coord.
    2d box xy are in image coord, normalized by width and height
    Point cloud are in egovehicle coord

    ::

       xy_image = K * [R|T] * xyz_ego

aerinkim

import numpy as np
import scipy.stats as stats

success_prob = 0.3
data = np.random.binomial(n=1, p=success_prob, size=1000) # sucess is 1, failure is 0.

# Domain θ
theta_range = np.linspace(0, 1, 1000)

# Prior P(θ)

aerinkim

import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [10, 7]

# Bell shape
x = np.linspace(0, 1, 10000)
y1 = beta.pdf(x, 2, 8)
y2 = beta.pdf(x, 5, 5)
y3 = beta.pdf(x, 8, 2)

aerinkim

from sklearn import datasets

def Kmeans(X, K):
    m = len(X)
    X_centroid = dict() # Save which sample belong to which cluster.
    X_centroid.fromkeys(range(0, m))
    C = dict()          # Save cluster's cordinate
    C.fromkeys(range(0, K))
    old_C = None        # Cache to save old C. Used for an early termination.
    # 1. Randomly initialize k centroids.