ppyPatternRecognition 0.0.3

Pattern Recognition Package

ppyPatternRecognition

[Should read this in github]

This is a library about AI model. All the model ideas are come from inclass lecture.

You can see the class github here which is created by Aj. Ekapol.

Table of content

• ppyPatternRecognition
  • Table of content
  • Provided model
  • How to install
  • K-means
  • Linear regression
  • Logistic regression
  • Simple Naïve Bayes

Provided model

• ppyPatternRecognition
  • Table of content
  • Provided model
  • How to install
  • K-means
  • Linear regression
  • Logistic regression
  • Simple Naïve Bayes

How to install

1. Install python
2. Install library using pip

   pip install -U ppyPatternRecognition
   

K-means

code here

example

from ppyPatternRecognition import Kmeans

df = pd.read_csv(...)

k_means = Kmeans()

# fit the model
labeled_df = k_means.fit(df, k=3)

# print the label
print(labeled_df['label'])

# get the last centroid
print(k_means.last_centroid)

• fit method will return the dataframe with label column
• last_centroid is the last centroid of the model after fitting

Linear regression

code here

example

from ppyPatternRecognition import LinearRegression

df = pd.read_csv(...)
X_train, y_train = ...

linear_regression = LinearRegression()

# fit the model
linear_regression.fit(X_train, y_train, epochs=1000, lr=0.01)

# predict
y_pred = linear_regression.predict(X_test)

• fit method will return the model itself
• predict method will return the predicted value

Logistic regression

code here

example

from ppyPatternRecognition import LogisticRegression

df = pd.read_csv(...)
X_train, y_train = ...

logistic_regression = LogisticRegression()

# fit the model
logistic_regression.fit(X_train, y_train, epochs=1000, lr=0.01)

# predict
y_pred = logistic_regression.predict(X_test)

• fit method will return the model itself
• predict method will return the predicted value

Simple Naïve Bayes

code here

example

from ppyPatternRecognition import SimpleBayesClassifier

df = pd.read_csv(...)
X_train, y_train = ...

simple_naive_bayes = SimpleBayesClassifier()

# fit the model
simple_naive_bayes.fit(X_train, y_train)

# predict
y_pred = simple_naive_bayes.predict(X_test)

• fit method will return the model itself
• fit_gaussian method will return the model itself
• predict method will return the predicted value
• predict_gaussian method will return the predicted value