Added neural network related things and other maths stuff

Image Processing/Vgg-16 Tutoriel.py

@@ -0,0 +1,37 @@
+"""
+#Vgg-16 architecture
+from keras.models import Sequential
+from keras.layers import Conv2D,MaxPooling2D,Flatten,Dense
+
+my_VGG16=Sequential()
+my_VGG16.add(Conv2D(64,(3,3),input_shape=(224,224,3),padding="same",activation="relu"))
+my_VGG16.add(Conv2D(64,(3,3),padding="same"),activation="relu")
+my_VGG16.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
+my_VGG16.add(Flatten())
+my_VGG16.add(Dense(4096,activation="relu"))
+my_VGG16.add(Dense(4096,activation="relu"))
+my_VGG16.add(Dense(100,activation="softmax"))
+
+
+
+from PIL import Image
+
+img=Image.open("simba.png")
+img.show()
+"""
+
+
+import keras
+
+from keras.preprocessing.image import load_img, img_to_array
+from keras.applications.vgg16 import VGG16, preprocess_input,decode_predictions
+model=VGG16()
+
+img=load_img("simba.png",target_size=(224,224))
+img=img_to_array(img)
+img=img.reshape((1,img.shape[0],img.shape[0],img.shape[2]))
+img=preprocess_input(img)
+
+y=model.predict(img)
+
+print("top3:",decode_predictions(y,top=3)[0])

Image Processing/main.py

@@ -0,0 +1,46 @@
+from PIL import Image,ImageFilter
+import numpy as np
+import matplotlib.pyplot as plt
+#import sys
+
+#print(sys.version)
+
+Img=Image.open("simba.png")
+img=np.array(Img)
+Img.show()
+
+imin,imax=200,255
+img2=[[255*(img[y][x]-imin)/(imax-imin) for x in range(len(img[0]))] for y in range(len(img))]
+
+noise=np.random.normal(0,7,img.shape)
+Img3=Image.fromarray(img+noise).convert("L")
+Img3.show()
+Img3.filter(ImageFilter.BoxBlur(1)).show()
+Img3.show()
+
+#Img2=Image.fromarray(img2).convert("L")
+#Img2.show()
+
+
+
+#img2.show()
+#img.show()
+
+#w,h=img.size
+#print(w,h)
+
+#print(img.__dict__)
+#print(rimg["im"])
+
+#for key,value in rimg.__dict__.items():
+    #print(key,":",value)
+
+#px_value=img.getpixel((20,100))
+#print(px_value)
+
+#mat=np.array(img)
+#print(mat)
+#print(mat.shape)
+
+n,bins,patches=plt.hist(img.flatten(),bins=range(256))
+plt.show()

Info/puissance de 2.py

@@ -0,0 +1,14 @@
+import math
+
+def puissancede2(number):
+    a=1
+    while a*2<number:
+        a*=2
+    return a
+
+print(puissancede2(513))
+
+
+puissance=lambda x:2**int(math.log(x)/math.log(2))
+
+print(puissance(513))

Matplotlib Tutorial/matplotlib tutorial.py

@@ -3,6 +3,12 @@ import matplotlib.pyplot as plt
 import csv
 import numpy as np
 
+import urllib
+import matplotlib.dates as mdates
+
+import sys
+print(sys.version)
+
 
 """
 x=[1,2,3]
@@ -88,14 +94,53 @@ x,y=np.loadtxt("text.txt",delimiter=",",unpack=True)
 plt.plot(x,y,label="loaded from file")
 """
 
+def bytespdate2num(fmt,encoding="utf-8"):
+    strconverter=mdates.strpdate2num(fmt)
+    def bytesconverter(b):
+        s=b.decode(encoding)
+        return strconverter(s)
+    return bytesconverter
+
 
 
 def graph_data(stock):
 
+    #fig=plt.figure()
+    #ax1=plt.subplot2grid((1,1),(0,0))
+
     stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
-    
-    plt.xlabel("x")
-    plt.ylabel("y")
+
+    source_code=urllib.request.urlopen(stock_price_url).read().decode()
+    #print(source_code)
+    stock_data=[]
+    split_source=source_code.split('\n')
+
+    for line in split_source:
+        split_line=line.split(",")
+        if len(split_line)==6:
+            if 'values' not in line and 'labels' not in line:
+                stock_data.append(line)
+
+    date,closep,highp,lowp,openp,volume=np.loadtxt(stock_data,
+                                             delimiter=",",
+                                             unpack=True,
+                                             #%Y=full year
+                                             #%y=partial year
+                                             #%m=number month
+                                             #%d=day
+                                             #%H=hours
+                                             #%M=minutes
+                                             #%S=seconds
+                                             #%m-%d-%Y
+                                             #converters={0: bytespdate2num('%Y%m%d')})
+                                             converters={0: lambda s : mdates.date2num(datetime.strptime(s.decode("utf-8"),'%Y%m%d'))})
+
+    plt.plot_date(date,closep,"-")
+
+
+
+    plt.xlabel("date")
+    plt.ylabel("price")
     plt.title("Interesting graph\nCheck it out!")
     plt.legend()
     plt.show()

Matplotlib Tutorial/matplotlib tutorial2.py

@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+from matplotlib import style
+import random
+
+style.use("fivethirtyeight")
+fig=plt.figure()
+ax1=fig.add_subplot(1,1,1)
+
+x=[i for i in range(100)]
+
+
+def animate(i):
+    y=[random.randint(0,10) for i in range(100)]
+    ax1.clear()
+    ax1.plot(x,y)
+
+ani=animation.FuncAnimation(fig,animate,interval=1000)
+plt.show()

Neural Network/NeuralNetwork.py

@@ -2,6 +2,8 @@ import numpy as np
 
 picture=np.random.randn(28,28)
 
+#import input_data
+
 class NeuralNetwork:
     def __init__(self,sizes=[784,784,28,10]):
         """Create a neural network using list of sizes."""
@@ -12,8 +14,8 @@ class NeuralNetwork:
 
     def evaluate(self,test_data):
         """Return number of successful predictions using test_datas list."""
-        results=[(np.argmax(self.predict(x)),y]) for (x,y) in test_data]
-        return sum(int(x==y) for (x,y) in results])
+        results=[(np.argmax(self.predict(x)),y) for (x,y) in test_data]
+        return sum([int(x==y) for (x,y) in results])
 
     def predict(self,input):
         """Return predictions for given input."""
@@ -59,7 +61,7 @@ class NeuralNetwork:
             weighted.append(z)
             x=sigmoid(z)
             activations.append(x)
-        
+
 
 
         return dB,dW

Panda Tutorial/panda.py

@@ -0,0 +1,3 @@
+import pandas as pd
+df=pd.read_csv("datasets/diamonds.csv",index_col=0)
+df.head()

Pyglet/Project 1/Triangle.py

@@ -0,0 +1,45 @@
+from pyglet.gl import *
+
+
+class Triangle:
+    def __init__(self):
+        self.triangle=[-0.5,-0.5, 0.0, 1.0, 0.0, 0.0,
+                        0.5,-0.5, 0.0, 0.0, 1.0, 0.0,
+                        0.0, 0.5, 0.0, 0.0, 0.0, 1.0]
+        self.vertex_shader_source="""
+        #version 330
+        in layout(location=0) vec3 position;
+        in layout(location=1) vec3 color;
+
+        out vec3 newColor;
+        void main()
+        {
+            gl_Position=vec4(position,1.0f)
+            newColor=color;
+        }
+        """
+
+        self.fragement_shader_source="""
+        #version 330
+        in vec3 newColor;
+        out vec4 outColor;
+        void main()
+        {
+        outColor=vec4(newColor,1.0f)
+        }
+        """
+
+        shader=OpenGL.GL.shaders.compileProgram(OpenGL.GL.shaders.compileShader(self.vertex_shader_source,GL_VERTEX_SHADER),
+                                                OpenGL.GL.shaders.compileShader(self.fragement_shader_source,GL_FRAGMENT_SHADER))
+
+        glUseProgram(shader)
+        vbo=GLuint(0)
+        glGenBuffers(1,vbo)
+        glBindBuffer(GL_ARRAY_BUFFER,vbo)
+        glBufferData(GL_ARRAY_BUFFER,72,(GLfloat*len(self.triangles)(*self.triangles),GL_STATIC_DRAW))
+        #positions
+        glVertexAttribPointer(0,3,GL_FLOAT,GL_FALSE,24,ctypes.c_void_p(0))
+        glEnableVertexAttribArray(0)
+
+        glVertexAttribPointer(1,3,GL_FLOAT,GL_FALSE,24,ctypes.c_void_p(12))
+        glEnableVertexAttribArray(0)

Pyglet/Project 1/Window.py

@@ -0,0 +1,21 @@
+from pyglet.gl import *
+from Triangle import Triangle
+
+class Window(pyglet.window.Window):
+    def __init__(self,*args,**kwargs):
+        super().__init__(*args,**kwargs)
+        self.set_minimum_size(400,300)
+        glClearColor(0,0,1,1)
+
+        self.triangle=Triangle()
+
+    def on_draw(self):
+        self.clear()
+        glDrawArrays(GL_TRIANGLES,0,3)
+
+    def on_resize(self,width,height):
+        glViewport(0,0,width,height)
+
+if __name__=="__main__":
+    window=Window(700,600,"My awesome window",resizable=True)
+    pyglet.app.run()

Pyglet/Pyglet learning.py

@@ -0,0 +1,55 @@
+from pyglet.gl import *
+import pyglet
+
+
+
+class Triangle:
+    def __init__(self):
+        self.vertices=pyglet.graphics.vertex_list(3,("v3f",[-0.5,-0.5,0.,0.5,-0.5,0.,0.,0.,0.5]),
+                                                      ("c3B",[100,200,220,200,110,100,100,250,100]))
+
+
+class Quad:
+    def __init__(self):
+        self.vertices=pyglet.graphics.vertex_list_indexed(4,[0,1,2,3,0],
+                                                           ("v3f",[-0.5,-0.5,0.0, 0.5,-0.5,0.0, 0.5,0.5,0.0, -0.5,0.5,0.0]),
+                                                           ("c3f",[0,1,0, 1,1,0, 1,0,0, 1,0,1]))
+
+class Quad2:
+    def __init__(self):
+        self.indices=[0,1,2,3,0]
+        self.vertex=[-0.5,-0.5,0.0, 0.5,-0.5,0.0, 0.5,0.5,0.0, -0.5,0.5,0.0]
+        self.color=[0,1,0, 1,1,0, 1,0,0, 1,0,1]
+        self.vertices=pyglet.graphics.vertex_list_indexed(4,self.indices,("v3f",self.vertex),("c3f",self.color))
+
+class Quad3:
+    def __init__(self):
+        self.indices=[0,1,2,3,0]
+        self.vertex=[-0.5,-0.5,0.0, 0.5,-0.5,0.0, 0.5,0.5,0.0, -0.5,0.5,0.0]
+        self.color=[0,1,0, 1,1,0, 1,0,0, 1,0,1]
+
+    def render(self):
+        self.vertices=pyglet.graphics.draw_indexed(4,GL_TRIANGLES,self.indices,("v3f",self.vertex),("c3f",self.color))
+
+
+class Window(pyglet.window.Window):
+    def __init__(self,*args,**kwargs):
+        super().__init__(*args,**kwargs)
+        self.set_minimum_size(400,300)
+        color=(255,0,0)
+        glClearColor(255,0,0,1.0)
+        self.triangle=Triangle()
+        self.quad=Quad3()
+
+    def on_draw(self):
+        self.clear()
+        #self.triangle.vertices.draw(GL_TRIANGLES)
+        #self.quad.vertices.draw(GL_QUADS)
+        self.quad.render()
+
+    def on_resize(self,width,height):
+        glViewport(0,0,width,height)
+
+if __name__=="__main__":
+    window=Window(1280,720,"First Pyglet Window",resizable=True)
+    pyglet.app.run()

Text/classes.py

@@ -237,7 +237,6 @@ if __name__=="__main__":
     #print(sentence-"fucking ")
     #sentence=MutableString(sentence)
     #print(sentence)
-    #sentence.append(" enorme test")
     #print(Str(sentence).title())
     #print(list(a))
     #print(a.remove("s"))

test addition.py

@@ -0,0 +1,2 @@
+def __+__(a,b):
+  return a+b