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UrusuLambda / stock-jp-sample.ipynb
Last active May 3, 2021 15:15
Try to get data from Stooq and calc 5/25/75 moving average, draw graph
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@UrusuLambda
UrusuLambda / tsne-pca-umap-2d-3d-plot.py
Created August 14, 2020 14:44
import tensorflow as tf
import numpy as np
#For 3D Plot
import pandas as pd
import plotly.express as px
#For PCA
from sklearn.decomposition import PCA
@UrusuLambda
UrusuLambda / umap-2d-3d-data.py
Last active August 14, 2020 17:16
#For 2D
x_train_tranformed_2d = umap.UMAP(n_neighbors=5, n_components=2).fit(x_train).embedding_
x_test_tranformed_2d = umap.UMAP(n_neighbors=5, n_components=2).fit(x_test).embedding_
#For 3D
x_train_tranformed_3d = umap.UMAP(n_neighbors=5, n_components=3).fit(x_train).embedding_
x_test_tranformed_3d = umap.UMAP(n_neighbors=5, n_components=3).fit(x_test).embedding_
@UrusuLambda
UrusuLambda / t-sne-2d-3d-data.py
Last active August 14, 2020 14:33
#For 2D
x_train_transformed_2d = TSNE(n_components=2).fit_transform(x_train)
x_test_transformed_2d = TSNE(n_components=2).fit_transform(x_test)
#For 3D
x_train_transformed_3d = TSNE(n_components=3).fit_transform(x_train)
x_test_transformed_3d = TSNE(n_components=3).fit_transform(x_test)
@UrusuLambda
UrusuLambda / PCA-2D-3D-data.py
Created August 14, 2020 14:30
x_train_transformed=PCA(n_components=8).fit_transform(x_train)
x_test_transformed=PCA(n_components=8).fit_transform(x_test)
#For 2D
x_train_transformed_3d = x_train_transformed
x_test_transformed_3d = x_test_transformed
#For 3D (Same to 2d)
x_train_transformed_2d = x_train_transformed
x_test_transformed_2d = x_test_transformed
@UrusuLambda
UrusuLambda / plot-3d-data.py
Created August 14, 2020 14:29
df_train=pd.DataFrame(x_train_transformed_3d[:, 0:3],columns=list("XYZ"))
df_train["label"]=np.array(y_train)
df_train['purpose'] = np.full(y_train.shape, "train")
df_test=pd.DataFrame(x_test_transformed_3d[:, 0:3],columns=list("XYZ"))
df_test["label"]=np.array(y_test)
df_test['purpose'] = np.full(y_test.shape, "test")
df_merged = pd.concat([df_train, df_test])
fig = px.scatter_3d(df_merged, x='X', y='Y', z='Z', color='label', symbol='purpose', size_max=4, opacity=0.7)
@UrusuLambda
UrusuLambda / plot-2d-data.py
Created August 14, 2020 14:27
plt.scatter(x_train_transformed_2d[:,0],x_train_transformed_2d[:,1],s=20,c=y_train,cmap='tab10', marker="o", alpha=0.3)
plt.scatter(x_test_transformed_2d[:,0],x_test_transformed_2d[:,1],s=20,c=y_test,cmap='tab10', marker="x", alpha=0.3)
plt.colorbar()
@UrusuLambda
UrusuLambda / mnist-get-data.py
Created August 14, 2020 14:26
import tensorflow as tf
import numpy as np
#For 3D Plot
import pandas as pd
import plotly.express as px
#For PCA
from sklearn.decomposition import PCA
@UrusuLambda
UrusuLambda / scatter-3d-tensorflow-mnist-datas.py
Created August 14, 2020 10:59
MNIST Data plot in interactive 3d for ipython notebook
from tensorflow.keras.datasets import mnist
from sklearn.manifold import TSNE
import pandas as pd
import plotly.express as px
#load and adjust mnist data | Mnistのデータをロード
(_, _), (x_test, y_test) = mnist.load_data()
_, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)
_, x_test = x_train / 255., x_test / 255.
@UrusuLambda
UrusuLambda / scatter-3d-tensorflow-datas.py
Created August 14, 2020 10:49
import pandas as pd
import plotly.express as px
#x_test.shape : (1000, 3)
df=pd.DataFrame(x_test,columns=list("XYZ"))
#y_test.shape : (1000,)
df["label"]=np.array(y_test)
#plot here
fig = px.scatter_3d(df, x='X', y='Y', z='Z', color='label', size_max=4, opacity=0.7)