Precepts… Five, no eight, better make it sixteen « Doing It Wrong

Precepts… Five, no eight, better make it sixteen

by James Martini

Categories: Buddhism

Tags: Sila, wis, zen

Comments: Comments Off

Published on: March 14, 2019

Coming away from the first class on the Bodhisattva Precepts at Buddha Eye Temple I have the following to roll around in my head for a while.

The Bodhisattva Precepts used in the Soto Zen tradition encompass and expand on the ethics / sila encompassed in the Noble Eightfold Path. The sixteen Bodhisattva Precepts include multiple precepts in three groups following the tradition of Buddhism as a list of lists. For the householder rather than the monastic, sila i.e. morality or virtue should be based on an inquiry into what advances oneself and others towards realization rather than simply an adherence to a set of rules.

Taking Refuge (3)

Refuge in Buddha
Refuge in Dharma
Refuge in Sangha

Three Pure Precepts (3)

Embracing Codes and Forms

This is the abode of the law of all Buddhas
This is the basis of the law of of all Buddhas
– Dogen
Embracing Good Dharmas

This is the Dharma of unsurpassed complete perfect awakening,
The Path of practicing and being practiced
– Dogen
Embracing Living Beings

This is transcending ordinary and sacred,
Taking self and others across
-Dogen

Ten Grave Precepts (10)

Not Killing
Not Stealing
Not Misusing Sexuality
Not Speaking Falsely
Not Dealing in Intoxicants
Not Discussing Faults
Not Praising Self and Criticizing Others
Not Being Stingy with Dharma and Wealth
Not Being Angry
Not Disparaging the Three Treasures

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from sklearn import neighbors, datasets, preprocessing
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# load the data into training and test sets
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from sklearn import neighbors, datasets, preprocessing

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# load the data into training and test sets

iris = datasets.load_iris()

X, y = iris.data[:, :2], iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=13)

# scale the data

scaler = preprocessing.StandardScaler()

scaler.fit(X_train)

X_train = scaler.transform(X_train)

X_test = scaler.transform(X_test)

# train a k-nearest neighbors model

knn = neighbors.KNeighborsClassifier(n_neighbors = 5)

knn.fit(X_train, y_train)

# Test the model

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