Dropbysometime
It signifies that while chance can be a catalyst or inspiration, it cannot be eliminated or controlled, even in artistic creation or the unfolding of life itself.
Here's a more detailed breakdown:
Chance as a Creative Force: Mallarmé uses the "throw of the dice" to represent chance, randomness, and the unpredictable nature of existence.
Inability to Eradicate Chance: The poem argues that even with deliberate effort (like a throw of the dice), one cannot fully overcome or abolish the influence of chance.
Poetic Struggle:
In the context of the poem, this struggle to control or eliminate chance reflects the poet's attempt to capture the absolute in language, which he ultimately finds impossible according to https://www.ebsco.com/research-starters/history/dice-throw-s....
Modernist Significance: This idea, along with the poem's innovative typography, made it a foundational work for modernist poetry, emphasizing the limitations of human control and the power of chance in both art and life.
Beyond the Literal: The phrase is not just about literal dice or a game of chance, but a metaphor for the human condition and the inherent uncertainties of existence.
Took from AI. Blasphemy
Thanks bryanrasmussen. We can all relate. But how can one live a happy life when chance(good luck/bad lucl) play such an important role (path dependence)? . Anyway love the poem style, look and fell like modern HTML.
p.s. We hack/programmers "do not" take yourselves so seriously, It all chance, maybe. Thank Yoy
Found the GitHub repository code https://github.com/AzazHassankhan/Machine-Learning-based-Tra...
Made some changes from line 9 to 70 . Usee yfinance instead of alpaca Replace all code with code below until line# 70
import plotly.offline as pox
import plotly.graph_objs as go
import numpy as np
import talib as tl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import talib as ta
from sklearn.model_selection import train_test_split
from sklearn.metrics import
accuracy_score,classification_report
#import alpaca_trade_api as tradeapi
#from alpaca_trade_api import TimeFrame, TimeFrameUnit
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import seaborn as sns
from matplotlib.pyplot import figure
from statsmodels.tsa.stattools import adfuller
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier
import yfinance as yf
from datetime import datetime
symb = "TSLA"
start = datetime(2021, 10, 18, 9, 30, 0)
end = datetime(2021, 10, 18, 10, 30, 0)
df =yf.download("TSLA", period="1mo",interval ="15m")
next=df.copy()
next.tail()
df['close']=df['Close']
df['high']=df['High']
df['low']=df['Low']
df['open']=df['Open']
df['volume']=df['Volume']