rouseguy

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

• NEVER use meta-phrases (e.g., "let me help you", "I can see that").
• NEVER summarize unless explicitly requested.
• NEVER provide unsolicited advice.
• NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
• ALWAYS be specific, detailed, and accurate.

rouseguy

absl-py==0.8.0
altair==3.2.0
annoy==1.16.0
appnope==0.1.0
asn1crypto==0.24.0
astor==0.8.0
attrs==19.1.0
backcall==0.1.0
bleach==3.1.0
blis==0.2.4

rouseguy

rouseguy

import numpy as np
import pandas as pd

import altair as alt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label(): 
    labels = {

rouseguy

X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
for i in range(0, int(len(data)/SEQ_LENGTH)):
    X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
    X_sequence_ix = [char_to_ix[value] for value in X_sequence]
    input_sequence = np.zeros((SEQ_LENGTH, VOCAB_SIZE))
    for j in range(SEQ_LENGTH):
        input_sequence[j][X_sequence_ix[j]] = 1.
    X[i] = input_sequence

rouseguy

import numpy as np

def input_generate_data(data, SEQ_LENGTH=20, VOCAB_SIZE=30, char_to_ix = {}):
    X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

    y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
    
    for i in range(0, int(len(data)/SEQ_LENGTH)):
        X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
        X_sequence_ix = [char_to_ix[value] for value in X_sequence]

rouseguy

import numpy as np

def input_generate_data(data, SEQ_LENGTH=SEQ_LENGTH, VOCAB_SIZE=VOCAB_SIZE, char_to_ix = char_to_ix):
    X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

    y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
    
    for i in range(0, int(len(data)/SEQ_LENGTH)):
        X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
        X_sequence_ix = [char_to_ix[value] for value in X_sequence]

rouseguy

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from tensorflow.examples.tutorials.mnist import input_data
import os

# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label(): 
    labels = {

rouseguy

import numpy as np

def fizzbuzz(number):
    if number % 15 == 0:
        return np.array(["fizzbuzz"], dtype="object")
    elif number % 5 == 0:
        return np.array(["buzz"], dtype="object")
    elif number % 3 == 0:
        return np.array(["fizz"], dtype="object")
    else:

rouseguy

# If you come from bash you might have to change your $PATH.
# export PATH=$HOME/bin:/usr/local/bin:$PATH

# Path to your oh-my-zsh installation.
export ZSH=/Users/bsubrama/.oh-my-zsh

# Set name of the theme to load. Optionally, if you set this to "random"
# it'll load a random theme each time that oh-my-zsh is loaded.
# See https://github.com/robbyrussell/oh-my-zsh/wiki/Themes
ZSH_THEME="robbyrussell"