| import tensorflow as tf | |
| import tensorflow_quantum as tfq | |
| import cirq | |
| import sympy | |
| import numpy as np | |
| nwires, nlayers = 6, 3 | |
| qubits = [cirq.GridQubit(0, i) for i in range(nwires)] | |
| symbols = sympy.symbols("params_0:" + str(nlayers * nwires * 2)) |
| import tensorflow as tf | |
| ## note tape.watch() cannot accept ragged tensor as input | |
| rt = tf.ragged.constant([[1.], [2., 3.]]) | |
| # rt = rt.to_tensor() | |
| with tf.GradientTape() as t: | |
| t.watch(rt.values) | |
| loss = 2*rt[0,0]+rt[1,1]**3 | |
| g_flatten = t.gradient(loss, rt.values) | |
| g = rt.with_values(g_flatten) | |
| print(g) |
| import xalpha as xa | |
| import pandas as pd | |
| import datetime as dt | |
| def _percent(r): | |
| return round((r-1)*100, 2) | |
| def index_analysis(code, start, end): | |
| dfe = xa.get_daily("peb-"+code, end=end, prev=20).iloc[-1] | |
| end = dfe["date"].strftime("%Y%m%d") |
| @tf.custom_gradient | |
| def idn(x): | |
| def grad(*dx): | |
| return tf.zeros_like(dx[0]) | |
| return x, grad | |
| def probexp(beta, s): | |
| return tf.exp(-beta*s) | |
| def obss(beta, s): |
| #! /bin/bash | |
| ips=("8.8.8.8" | |
| "baidu.com") | |
| webs=("http://10.0.0.1:8080") | |
| address="user@mails.domain" | |
| for ip in "${ips[@]}"; do |
This is a step-by-step tutorial for setting up your own conda feedstock from scratch. I started with the instructions here:
As a novice, I found that the above tutorial lacked sufficient detail. So, I decided to document the process that I used for my own future reference.
Forked version: edit part in italic