#Zytl.AI (First General AI?)

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vestal hound
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If anyone would like a demo lmk

vestal hound
vestal hound
vestal hound
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DS1000 #2: ARIMA Forecast & Plot - Generated & Valid (Plot shown, forecast printed)
Mutated Code:

import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.tsa.arima.model import ARIMA
import matplotlib.pyplot as plt

# Dummy time series data
np.random.seed(42)
ts_data = np.cumsum(np.random.randn(50))  # Random walk
ts = pd.Series(ts_data, index=pd.date_range('2020-01-01', periods=50, freq='D'))

# Task: Fit ARIMA(1,1,1), forecast 5 steps, plot
model = ARIMA(ts, order=(1,1,1))
fitted = model.fit()
forecast = fitted.forecast(steps=5)
plt.plot(ts, label='Observed')
plt.plot(forecast, label='Forecast')
plt.title('ARIMA Time Series Forecast')
plt.legend()
plt.show()
print(f"Forecast: {forecast.tolist()}")```
vestal hound
vestal hound
# 

R2 Score: 0.9941
DS1000 #4: Linear Regression & Plot - Generated & Valid (Line fit shown, R2 printed)
Mutated Code:

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Dummy data for regression
np.random.seed(42)
X = np.random.rand(100, 1) * 10
y = 2 * X.squeeze() + np.random.randn(100) * 0.5 + 5  # Linear with noise

# Task: Fit LinearRegression, predict, plot line/residuals, print R2
model = LinearRegression()
model.fit(X, y)
y_pred = model.predict(X)
r2 = model.score(X, y)
plt.scatter(X, y, label='Data')
plt.plot(X, y_pred, color='red', label='Fit Line')
plt.title('Linear Regression Fit')
plt.xlabel('X')
plt.ylabel('y')
plt.legend()
plt.show()
print(f"R2 Score: {r2:.4f}")

Exec Time: 665.743 ms
Total Test Time: 679.241 ms```
vestal hound
#

Zytl.AI evos to PCA dim reduction in 362ms: 30% variance captured

image0.jpg image1.jpg
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Variance Ratio: [0.16110575281816705, 0.13898059329392343]
Reduced shape: (100, 2)
DS1000 #5: PCA Reduction & Plots - Generated & Valid (Variance bar & scatter shown)
Mutated Code:

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# Dummy high-dim data (100 samples, 10 features)
np.random.seed(42)
data = np.random.rand(100, 10) * 10

# Task: Fit PCA(n_components=2), transform, plot explained variance ratio & reduced scatter
pca = PCA(n_components=2, random_state=42)
reduced = pca.fit_transform(data)
variance_ratio = pca.explained_variance_ratio_
plt.bar(range(1, 3), variance_ratio, alpha=0.8)
plt.title('PCA Explained Variance Ratio')
plt.xlabel('Principal Component')
plt.ylabel('Variance Ratio')
plt.show()
plt.scatter(reduced[:, 0], reduced[:, 1], c=np.arange(len(reduced)), cmap='viridis')
plt.title('PCA Reduced Data (2D)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.colorbar(label='Sample Index')
plt.show()
print(f"Variance Ratio: {variance_ratio.tolist()}")
print(f"Reduced shape: {reduced.shape}")

Exec Time: 354.302 ms
Total Test Time: 362.032 ms```
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Zytl.AI chains Gini gen → KMeans clusters in 221ms

image0.jpg