Added Lstm example for stock predection (#1908)

* Added Lstm example for stock predection * Changes after review * changes after build failed * Add Kiera’s to requirements.txt * requirements.txt: Add keras and tensorflow * psf/black Co-authored-by: Christian Clauss <cclauss@me.com>
TheAlgorithms · May 7, 2020 · 8a8527f1bd0ec2641a1d09c6ace5a73d7f7675f0 · 8a8527f
1 parent 4acc28b
commit 8a8527f1bd0ec2641a1d09c6ace5a73d7f7675f0
Unified Split

Showing with 1,317 additions and 1 deletion.

+56 −0 machine_learning/lstm/lstm_prediction.py

+1,259 −0 machine_learning/lstm/sample_data.csv

+2 −1 requirements.txt
diff --git a/machine_learning/lstm/lstm_prediction.py b/machine_learning/lstm/lstm_prediction.py
@@ -0,0 +1,56 @@
+"""
+    Create a Long Short Term Memory (LSTM) network model
+    An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
+    * http://colah.github.io/posts/2015-08-Understanding-LSTMs
+    * https://en.wikipedia.org/wiki/Long_short-term_memory
+"""
+
+from keras.layers import Dense, LSTM
+from keras.models import Sequential
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+
+if __name__ == "__main__":
+    """
+    First part of building a model is to get the data and prepare
+    it for our model. You can use any dataset for stock prediction
+    make sure you set the price column on line number 21.  Here we
+    use a dataset which have the price on 3rd column.
+    """
+    df = pd.read_csv("sample_data.csv", header=None)
+    len_data = df.shape[:1][0]
+    # If you're using some other dataset input the target column
+    actual_data = df.iloc[:, 1:2]
+    actual_data = actual_data.values.reshape(len_data, 1)
+    actual_data = MinMaxScaler().fit_transform(actual_data)
+    look_back = 10
+    forward_days = 5
+    periods = 20
+    division = len_data - periods * look_back
+    train_data = actual_data[:division]
+    test_data = actual_data[division - look_back :]
+    train_x, train_y = [], []
+    test_x, test_y = [], []
+
+    for i in range(0, len(train_data) - forward_days - look_back + 1):
+        train_x.append(train_data[i : i + look_back])
+        train_y.append(train_data[i + look_back : i + look_back + forward_days])
+    for i in range(0, len(test_data) - forward_days - look_back + 1):
+        test_x.append(test_data[i : i + look_back])
+        test_y.append(test_data[i + look_back : i + look_back + forward_days])
+    x_train = np.array(train_x)
+    x_test = np.array(test_x)
+    y_train = np.array([list(i.ravel()) for i in train_y])
+    y_test = np.array([list(i.ravel()) for i in test_y])
+
+    model = Sequential()
+    model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
+    model.add(LSTM(64, input_shape=(128, 1)))
+    model.add(Dense(forward_days))
+    model.compile(loss="mean_squared_error", optimizer="adam")
+    history = model.fit(
+        x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
+    )
+    pred = model.predict(x_test)