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# Brian Ray <brianhray@gmail.com>
# @brianray
# https://medium.com/@brianray_7981/
# parser for POSH Syntax http://brianray.github.io/posh-syntax/
import re


def transition_skip(fsm_obj):
    pass

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{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: Cloud Function Emulate",
            "type": "python",
            "request": "launch",
            "module": "functions_framework",
            "justMyCode": false, 
            "args": [

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graph LR
  A((Start))--take Survey-->B((Goal Setting))
  B((Goal Setting))--1 on 1 coach-->C((Working On Concentation))
  C((Working On Concentation))--1 on 1 coach weekly-->C((Working On Concentation))

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Scenarios

1. Jailbreaking for Data Leaked from Hacker Actor: the hacker gets data out of the model by pure prompt manipulation,
2. Intrusion from Hacker Actor: The malicious instructions pass through the application and can instruct the extension services to do bad things.
3. Data poisoning for Training data: The source data used to train the LLM has malicious content before it is trained.
4. Prompt Poising: hidden content or injected content unintentional from an innocent bystander.

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[name]		By: [by]
Data Access		...
Runtime and Cost		...
Namespaces		...
Advanced Types		...
Scheduling		...
Streaming		...
Feature Transform		...
Time Sensitivity		...

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Databricks Feature Store		By: Databricks
Data Access		...
Runtime and Cost		...
Namespaces		...
Advanced Types		...
Scheduling		...
Streaming		...
Feature Transform		...
Time Sensitivity		...

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from nltk.downloader import Downloader

dl = Downloader()

def format(x, fields, type_name, delim):
    x = x.__dict__
    out = delim
    for col in fields.split(delim):
        if col == 'type':
            out += type_name

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prophet_model = Prophet(
    growth='linear',
    changepoints=None,
    n_changepoints=25,
    changepoint_range=0.8,
    yearly_seasonality=10,  # Fourier
    weekly_seasonality=3,  # Fourier
    daily_seasonality='auto',
    seasonality_mode='additive',
    seasonality_prior_scale=10.0,

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import scipy.signal as sg

def decimate_rolling(df, size=100):
    out = []
    for name in names[:n_samples]:
        inds = np.arange(0, len(df), 8000) ## TODO
        for i, j in zip(inds[:-1], inds[1:]):
            dij = d[i:j]
            dij = sg.decimate(dij, 2, ftype="iir")[::2]
            out.append(dij)

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