JakubMifek

quotes = ['"', '❞', '❝', '„', '“', '‟', '”', '”', '⹂']
desired_quotes = ('„', '“')


def fix_quotes(text: str) -> str:
    text = text.replace(',,', '"')
    text = text.replace("''", '"')
    j = 0
    T = [t for t in text]
    for i in range(len(T)):

JakubMifek

Hello, traveler o/

JakubMifek

API

Option 1 - No path parameters (except CRUD)

POST /api/performance/v1/test-entities/action

• cannot contain path parameters - collisions are unavoidable
• ALL parameters are in query
• CRUD operations are denoted with crud prefix (/api/performance/crud/v1/...)
• CRUD may contain path parameters (they do not collide)

Option 2 - Header parameters

POST /api/performance/v1/test-entities/

JakubMifek

#!/usr/bin/env python3
#
# All team solutions **must** list **all** members of the team.
# The members must be listed using their ReCodEx IDs anywhere
# in a comment block in the source file (on a line beginning with `#`).
#
# You can find out ReCodEx ID in the URL bar after navigating
# to your User profile page. The ID has the following format:
# 310a5c89-3ea1-11e9-b0fd-00505601122b
# 90257956-3ea2-11e9-b0fd-00505601122b

JakubMifek

#!/usr/bin/env python3
import numpy as np
import tensorflow as tf

import decoder
from morpho_dataset import MorphoDataset

class Network:
    def __init__(self, args, num_source_chars, num_target_chars):
        class Model(tf.keras.Model):

JakubMifek

Morpho

Batch:

• forms
• lemmas
• tags

We have sentences and want to predict classes (nouns, verbs, ..)

• we can specify mask which specifies number of words in sentence and "disables" parts of NN which will not be used (since we have less data). This will influence even the loss function
• 1 for used place, 0 for not used (tf.keras.layers.Masking)

JakubMifek

5. 4. 2019

Feature engineering - increases data dimensionality

JakubMifek

jakub@Marvn:~/Projects$ lb4 project
? Project description: project
? Project root directory: project
? Application class name: ProjectApplication
? Select features to enable in the project Enable tslint, Enable prettier, Enable moch
a, Enable loopbackBuild, Enable vscode, Enable repositories, Enable services
   create .npmrc
   create .prettierignore
   create .prettierrc
   create DEVELOPING.md

JakubMifek

attach(train)
m.tree <- rpart(mpg01 ~ ., train)
rpart.plot(m.tree)

prediction.m.tree <- predict(m.tree, test, type="class")
table(prediction.m.tree)
m.tree.cm <- table(prediction.m.tree, test$mpg01)
m.tree.cm
message("Accuracy = ", sum(diag(m.tree.cm))/78)

JakubMifek

import io
import json
import sys


def tokenize(line):
  return [token.strip() for token in line.split(':')]


def getSnapshot(line, idx, f):