From 6f593da87ac390dcba0a37f389b7322a30b437d5 Mon Sep 17 00:00:00 2001
From: bloodstalker <thabogre@gmail.com>
Date: Mon, 28 May 2018 03:47:34 +0430
Subject: update

---
 cnn.py | 104 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100755 cnn.py

(limited to 'cnn.py')

diff --git a/cnn.py b/cnn.py
new file mode 100755
index 0000000..7d402c3
--- /dev/null
+++ b/cnn.py
@@ -0,0 +1,104 @@
+#!/usr/bin/python3
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import json
+import numpy as np
+import os
+import pandas as pd
+import urllib3
+import requests
+from pathlib import Path
+from sklearn.preprocessing import MinMaxScaler
+
+def SigHandler_SIGINT(signum, frame):
+    print()
+    sys.exit(0)
+
+class Argparser(object):
+    def __init__(self):
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--string", type=str, help="string")
+        parser.add_argument("--bool", action="store_true", help="bool", default=False)
+        parser.add_argument("--dbg", action="store_true", help="debug", default=False)
+        self.args = parser.parse_args()
+
+class PastSampler(object):
+    def __init__(self, N, K, sliding_window=True):
+        self.N = N
+        self.K = K
+        self.sliding_window = sliding_window
+
+    def transform(self, A):
+        M = self.N + self.K
+        if self.sliding_window:
+            I = np.arange(M) + np.arange(A.shape[0] - M + 1).reshape(-1,1)
+        else:
+            if A.shapep[0]%M == 0:
+                I = np.arange(M) + np.arange(0, A.shape[0], M).reshape(-1,1)
+            else:
+                I = np.arange(M) + np.arange(0, A.shape[0] - M, M).reshape(-1,1)
+
+        B = A[I].reshape(-1, M*A.shape[1], A.shape[2])
+        ci = self.N*A.shape[1]
+        return B[:, :ci], B[:, ci:]
+
+def getData(symbol_str):
+    data_file = Path("./" + symbol_str + ".csv")
+    original_columns =["close", "date", "high", "low", "open"]
+    new_columns = ["Close", "Timestamp", "High", "Low", "Open"]
+    columns = ["Close"]
+    if data_file.is_file():
+        original_data_file = pd.read_csv(data_file).loc[:, columns]
+        return pd.read_csv(data_file).loc[:, columns], original_data_file
+    else:
+        url = "https://poloniex.com/public?command=returnChartData&currencyPair=USDT_" + symbol_str + "&start=1356998100&end=9999999999&period=300"
+        r = requests.get(url)
+        d = json.loads(r.content.decode("utf-8"))
+        df = pd.DataFrame(d)
+
+        df = df.loc[:, original_columns]
+        df.columns = new_columns
+        df.to_csv(symbol_str + ".csv", index=None)
+        df = pd.read_csv(data_file)
+        time_stamps = df["Timestamp"]
+        df = df.loc[:, columns]
+        original_data_file = pd.read_csv(data_file).loc[:, columns]
+        return df
+
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+    columns = ["Close"]
+    btc_df, orig_btc = getData("BTC")
+    eth_df, orig_eth = getData("ETH")
+    scaler = MinMaxScaler()
+    for c in columns:
+        btc_df[c] = scaler.fit_transform(btc_df[c].values.reshape(-1, 1))
+        eth_df[c] = scaler.fit_transform(eth_df[c].values.reshape(-1, 1))
+
+    A = np.array(eth_df)[:,None,:]
+    original_A = np.array(orig_eth)[:,None,:]
+    time_stamps = np.array(time_stamps)[:, None, None]
+
+def main():
+    argparser = Argparser()
+    if argparser.args.dbg:
+        try:
+            premain(argparser)
+        except Exception as e:
+            print(e.__doc__)
+            if e.message: print(e.message)
+            variables = globals().copy()
+            variables.update(locals())
+            shell = code.InteractiveConsole(variables)
+            shell.interact(banner="DEBUG REPL")
+    else:
+        premain(argparser)
+
+if __name__ == "__main__":
+    main()
-- 
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