diff --git a/.idea/misc.xml b/.idea/misc.xml
index 8656114..6649a8c 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,5 +3,5 @@
-
+
\ No newline at end of file
diff --git a/.idea/other.xml b/.idea/other.xml
new file mode 100644
index 0000000..640fd80
--- /dev/null
+++ b/.idea/other.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index e8d76cf..4fca350 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,13 +1,33 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+
+
-
-
+
+
-
@@ -21,6 +41,19 @@
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/wozek.iml b/.idea/wozek.iml
index fa80a76..c4b5840 100644
--- a/.idea/wozek.iml
+++ b/.idea/wozek.iml
@@ -4,10 +4,10 @@
-
+
-
-
+
+
\ No newline at end of file
diff --git a/Assiging/feature_hashing.py b/Assiging/feature_hashing.py
deleted file mode 100644
index 479b42f..0000000
--- a/Assiging/feature_hashing.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from sklearn.feature_extraction import FeatureHasher
-from data import create_data_dict
-
-data = create_data_dict()
-print(data)
\ No newline at end of file
diff --git a/coder/test.jpg b/coder/test.jpg
new file mode 100644
index 0000000..5a81770
Binary files /dev/null and b/coder/test.jpg differ
diff --git a/coder/train.py b/coder/train.py
new file mode 100644
index 0000000..f659b20
--- /dev/null
+++ b/coder/train.py
@@ -0,0 +1,27 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from numpy import asarray
+from sklearn import datasets
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+from PIL import Image
+
+#recznie napisane cyfry
+digits = datasets.load_digits()
+
+y = digits.target
+x = digits.images.reshape((len(digits.images), -1))
+
+x_train = x[:1000000]
+y_train = y[:1000000]
+x_test = x[1000:]
+y_test = y[1000:]
+
+mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
+ solver='sgd', tol=1e-4, random_state=1,
+ learning_rate_init=.1, verbose=True)
+
+mlp.fit(x_train, y_train)
+
+predictions = mlp.predict(x_test)
+print(accuracy_score(y_test, predictions))