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

Comments are closed.

Recent Posts

Cheating Irises by James Martini

HAL8999 7/100 I was sick yesterday but did spend some time looking over some “cheat sheets” that people had put together for various machine learning topics. Some were good, some were just stupid (I’m looking at you Machine Learning in Emoji). Also went through a very simply classifier based on the iris data set.

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
y_pred = knn.predict(X_test)
print(accuracy_score(y_test, y_pred))

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

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

y_pred = knn.predict(X_test)

print(accuracy_score(y_test, y_pred))

[…]

Transformers, more than mee… by James Martini
HAL8999 6/100 Watched the “Learn how to Learn” Google talk on youtube Updated the jupyter notebooks for handson-ml from github and read through the Ch2 notebook to address the CategoricalEncoder issue from yesterday Looked at a basic transformer Part of building a data pipeline is likely to include the creation of custom transformer classes to[…]
from handson-ml import BrokeAsFuck by James Martini
HAL8999 – 5/100 Today while going back through the Hands On Machine Learning book Ch2 I learned that the CategoricalEncoder referenced in the section on handling categorical attributes still isn’t in scikit-learn. I checked the reqirements.txt which shows scikit-learn=0.19.1. Checking my virtualenv, I should be good.

$ grep scikit-learn handson-ml/requirements.txt scikit-learn==0.19.1 $ pip freeze |grep scikit-learn scikit-learn==0.19.2

1
2
3
4

$ grep scikit-learn handson-ml/requirements.txt
scikit-learn==0.19.1
$ pip freeze |grep scikit-learn
scikit-learn==0.19.2

Turns out that the CategoricalEncoder isn’t going to[…]
Long days, no blog post by James Martini
HAL8999 – [3,4]/100 Chapter 2 of Hands on ML continues Creation of test sets Stratified sampling sklearn’s StratifiedShuffleSplit Visualizing data with matplotlib Coorelation coefficients Getting a good train-test split Since you can’t train a model and just expect it to work well right out of the box it’s standard practice to split off about 20%[…]

Categories

User

Not logged in
Log in

Log in

Username or Email Address

Password

Remember Me

Close

Welcome , today is Saturday, February 27, 2021