Learn Words Manager

In [1]:
from xv.kids.managers import LearnWordsManager
In [2]:
ke = LearnWordsManager()
ke
Out[2]:
140352221408000@LearnWordsManager

A module for words in English


Minimum Grade: 3
Maximum Grade: 12


Examples
--------
from xv.english import LearnWordsManager
root_folder = "books_text"
ke = LearnWordsManager(root_folder = root_folder, parent_depth = 3, verbose = False)
ke

ke.printProblemTypes()

ke.getRandomProblem()
ke.getRandomProblem(problem_type = 0)
...

ke.printProblem()
ke.printAnswer()
ke.printSolution()

doc_style: xv_doc

In [3]:
ke.printProblemTypes()

0. _problem_word_search
1. _problem_sight_words_pre_primary
2. _problem_sight_words_grade_1
3. _problem_sight_words_grade_2
4. _problem_count_one_to_ten
5. _problem_animals_by_alphabet
In [4]:
search = """
come 	I 	often 	through 	your love
"""
In [5]:
ke.getRandomProblem(problem_type = 0,
                   search = search,
                   )
Out[5]:

Run the following code to hear text and then write it.


ke.listen()
In [6]:
ke.listen()
Out[6]:
In [7]:
ke.printAnswer()
Out[7]:
come: cum
love: luv
often: off-en
through: throo

Sentences:


Ursula watched them come up the steps.
I went through rich scenes!
Don't people often come off together?
You still love him, I ast.
In [8]:
ke.printSolution()
Out[8]:

come: cum
love: luv
often: off-en
through: throo

Sentences:


Ursula watched them come up the steps.
I went through rich scenes!
Don't people often come off together?
You still love him, I ast.

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)
In [9]:
from IPython.display import HTML
n = len(ke._problemTemplates)
max_loop = 1
for j in range(0, max_loop):
    for i in range(n):
        problem_type = i
        display(HTML(f"<h2>problem_type: {problem_type}/{n-1} (loop {j}/{max_loop-1})</h2>"))
        ke.getRandomProblem(problem_type = problem_type, verbose = True)
        display(ke.printProblem())

        display(HTML(f"<h6>Answer:</h6>"))
        display(ke.printAnswer())

        display(HTML(f"<h6>Solution:</h6>"))
        display(ke.printSolution())
        pass

problem_type: 0/5 (loop 0/0)

Problem Template: _problem_word_search

Run the following code to hear text and then write it.


ke.listen()
Hint:
pass parameter search = 'word to search'.
You can pass a string containing words or a list of words.
Answer:
bodkin: bod-kin
napkin: nap-kin
sanitary napkin: sa-ni-tree nap-kin
pumpkin: pum(p)-kin

Sentences:


Those napkin rings were made by a prisoner.
Solution:

bodkin: bod-kin
napkin: nap-kin
sanitary napkin: sa-ni-tree nap-kin
pumpkin: pum(p)-kin

Sentences:


Those napkin rings were made by a prisoner.

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)

problem_type: 1/5 (loop 0/0)

Problem Template: _problem_sight_words_pre_primary

Run the following code to hear text and then write it.


ke.listen()
Answer:
a: a
a: ay
am: am
can: can

Sentences:


He is a bad man.
I am none of these things.
You can imagine the reaction.
Solution:

a: a
a: ay
am: am
can: can

Sentences:


He is a bad man.
I am none of these things.
You can imagine the reaction.

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)

problem_type: 2/5 (loop 0/0)

Problem Template: _problem_sight_words_grade_1

Run the following code to hear text and then write it.


ke.listen()
Answer:
their: thayr
there: thare
they: thay
thought: thawt

Sentences:


Ah, there's a secret!
We'd know if they were lookin' for you?
Mirah is rich with their oriental gifts.
And he thought of Sonia.
Solution:

their: thayr
there: thare
they: thay
thought: thawt

Sentences:


Ah, there's a secret!
We'd know if they were lookin' for you?
Mirah is rich with their oriental gifts.
And he thought of Sonia.

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)

problem_type: 3/5 (loop 0/0)

Problem Template: _problem_sight_words_grade_2

Run the following code to hear text and then write it.


ke.listen()
Answer:
off: off
often: off-en
one: wun
other: u-ther

Sentences:


Lad,' said Silver, 'no one's a-pressing of you.
Don't strike my head off!
Here the other guinea-pig cheered, and was suppressed.
This sort of thing often happens.
Solution:

off: off
often: off-en
one: wun
other: u-ther

Sentences:


Lad,' said Silver, 'no one's a-pressing of you.
Don't strike my head off!
Here the other guinea-pig cheered, and was suppressed.
This sort of thing often happens.

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)

problem_type: 4/5 (loop 0/0)

Problem Template: _problem_count_one_to_ten

Run the following code to hear text and then write it.


ke.listen()
Answer:
four
seven
eight
Six
Solution:

four
seven
eight
Six


One, two, three, four, five,
Once I caught a fish alive.
Six, seven, eight, nine, ten.
Then I let it go again.

Why did I let it go,
Because he bit my finger so!
Which finger did he bite?
This little finger on my right!

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)

problem_type: 5/5 (loop 0/0)

Problem Template: _problem_animals_by_alphabet

Run the following code to hear text and then write it.


ke.listen()
Answer:
Quail
Penguin
bird
Fox
Solution:

Quail
Penguin
bird
Fox


A is for Ant
B is for Bat
C is for Canary
D is for Duck
E is for Elephant
F is for Frog
G is for Gorilla
H is for Hippo
I is for Iguana
J is for Jaguar
K is for Kangaroo
L is for Lion
M is for Monkey
N is for Narwhal

O is for Owl
P is for Penguin
Q is for Quail
R is for Raccoon
S is for Squirrel
T is for Tiger
U is for Umbrella bird
V is for Vulture
W is for Whale
X is for Fox
Y is for Yak
Z is for Zebra

Code to convert text to speech:


Run the following command:
import IPython
IPython.display.Audio(ke.speech_file)

kindergarten

  1. Arithmetic
  2. Identify Pictures
  3. Learn Words

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  2. Plotting points
  3. Learning shapes, size and color
  4. Learning Coordinate Geometry
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Linear Equations

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Geometry

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  3. Cylinder geometry
  4. Disk geometry
  5. Open top box geometry
  6. Rectangle geometry
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Vectors

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  2. Vector implimentation 2 vectors
  3. Vector with numpy and sympy

Differential equations

  1. First order differential equations

Functions

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  2. Function plotting in 2D

Jacobian

  1. Jacobian

Lagrangian

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  2. Lagrange example

Waves

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Electromagnetism

  1. Field plot
  2. Electric fields
  3. Fields
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Optics

  1. Lens optics geometry
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Quantum mechanics concepts

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  3. Electron spin
  4. Position momentum and heisenberg
  5. The EPR paradox explanation
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Theory of relativity

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Kinematics

  1. Circular motion

Thermodynamics

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  4. ElementsManager
  5. EnvironmentManager
  6. MolarManager
  7. MoleculesManager
  8. PeriodicTableManager
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  11. VisualizationManager

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  2. Word Manager
  3. Grammar Manager
  4. Tense Manager
  5. Vocabulary Manager
  6. Learn Language Manager
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  2. Plot World, Continent and Cities

Animation

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Plotting

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  2. Single Line Plots
  3. Bar Plots
  4. Histograms Plots
  5. Types of Plotting
  6. Seaborn Introduction
  7. Seaborn Scatterplot
  8. Seaborn Barplot
  9. Seaborn Boxplot
  10. Seaborn Pairplot
  11. Seaborn Heatmap
  12. Seaborn Seaborn Pandas
  13. Seaborn Plot a Simple Linear Relationship Between Two Variables
  14. Seaborn Displot
  15. Seaborn Palette
  16. Three-Dimensional Plotting in Matplotlib
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  18. Data Visualization With Seaborn - Part 1
  19. Draw Concrete Jungle In Matplotlib
  20. Draw Beautiful Eyes in Python Using the Matplotlib Module
  21. Celebrate Valentine With Your Partner and Python

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  2. SVG Shapes Advanced
  3. Animate Circle With SVG
  4. SVG Animation
  5. SVG Text
  6. Can We Write Alphabets With Help of SVG?

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  10. Regular Expression Operations in Python
  11. Creating and Writing to Different Type of Files in Python
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  3. Sequence to Sequence Learning With Neural Networks To Perform Number Addition
  4. Image Classification Model MobileNet V2 from TensorFlow Hub
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  6. Sentiment Analysis for Hotel Reviews With NLTK and Keras
  7. Simple Sequence Prediction With LSTM
  8. Image Classification With ResNet50 Model
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  10. Predict Diabetes With Machine Learning Algorithms
  11. TensorFlow Build Custom Convolutional Neural Network With MNIST Dataset
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PySpark

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  2. Role of StringIndexer and Pipelines in PySpark ML Feature - Part 1
  3. Role of OneHotEncoder and Pipelines in PySpark ML Feature - Part 2
  4. Feature Transformer VectorAssembler in PySpark ML Feature - Part 3
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PyTorch

  1. Build the Neural Network with PyTorch
  2. Image Classification with PyTorch
  3. Twitter Sentiment Classification In PyTorch
  4. Training an Image Classifier in Pytorch

Natural Language Processing

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  2. Handling Text For Machine Learning
  3. Extracting Text From PDF File in Python Using PyPDF2
  4. How to Collect Data Using Twitter API V2 For Natural Language Processing
  5. Converting Text to Features in Natural Language Processing
  6. Extract A Noun Phrase For A Sentence In Natural Language Processing

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