reshape drops

of matrices >>> import NumPy as np>>> B = np.arange (+). Reshape (4, 4) >>> for row in B: ... Print (Row) ... [0 1 2 3] [4 5 6 7] [8 9 11][12 15]>>> for node in B.flat: ... Print (node) ... 0123456789101112131415 Special operations of matrices Change matrix shape--reshape >>> a = Np.floor (Ten * Np.random.random ((3,4))) >>> Aarray ([[6., 5., 1., 5.], [5., 5., 8., 9.], [5., 5., 9.,

= ' Level1 '), right_on= ' Level2 ')) The result is: Two data frames if the column names are different, we can connect the data by specifying the letf_on and right_on two parameters Print (Pd.merge (data3,data4,left_on= ' level1 ', right_on= ' Level2 ', how= ' left ')) The result is: Other detailed parameter description Overlapping data merging Sometimes we encounter overlapping data that needs to be merged, so we can use the Comebine_first function. Data3 = PD. DataFrame ({' Level ': ['

(GL_LIGHT0 ); GlEnable (GL_DEPTH_TEST ); } /* Call the GLUT function to draw a ball */ Void display (void) { GlClear (GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT ); Glusolidsphere (1.0, 40, 50 ); GlFlush (); } /* Defines the reshape function of GLUT. w and h indicate the width and height of the current window respectively */ Void reshape (int w, int h) { GlViewp

: If the column names in the two data boxes are different, we can connect the data by specifying the letf_on and right_on parameters. print(pd.merge(data3,data4,left_on='level1',right_on='level2',how='left')) Result: Other detailed parameter descriptions Merge overlapping data Sometimes we may encounter overlapping data that needs to be merged. In this case, we can use the comebine_first function. data3 = pd.DataFrame({'level':['a','b','c','d'], 'numeber1':[1,3,5,np.nan]}) data4=pd.D

]}) data4=pd.DataFrame({'level':['a','b','c','e'], 'numeber2':[2,np.nan,6,10]}) print(data3.combine_first(data4)) Result: The usage here is similar to np. where (isnull (a), B,) Data remodeling and axial rotation This content is mentioned in the previous pandas article. Data remodeling mainly uses the reshape function, while rotation mainly uses the unstack and stack functions. data=pd.DataFrame(np.arange(12).

#define Glut_disable_atexit_hack#pragma comment (lib, "Glew32.lib")#include #include #include #include #include #include #include #include #include #include void render (void);void Keyfunc (unsigned char, int, int);int resource_group (void);int load_data_files (void);int Create_window (void);void reshape (int w, int h);int window_id;bool keep_running = true; int main (int argc, char *argv[]) { Glutinit (ARGC, argv); Glutinitdisplaymode (glut_depth

The matrix M in the vertical direction and aggregates the H-times in the horizontal directionBlkdiag (A, b) creates a block diagonal matrix with a, and a block The subscript of the MATLAB array starts with "1", which is different from the C language. MATLAB Specifies that the storage order of matrix elements in memory is stored in the order of the columns , that is, after the 1th column is saved, the 2nd column is saved, and so on. For example, there is a 3x4-order matrix B, which

Content from UFLDL, code reference from Tornadomeet CnnCost.m1.Forward propagationConvolvedfeatures = Cnnconvolve (Filterdim, numfilters, images, Wc, BC); %for the first arrow activationspooled= Cnnpool (Pooldim, convolvedfeatures);corresponds to a second arrow%corresponds to the 3rd arrow, that is, the flat roll activationspooled=reshape (activationspooled,[],numimages);%the probability of the beginning of the calculation of Softmax belongs to variou

(LambdaX:X * * 2))#add a layer that returns the concatenation# of the positive part of the the input and#The opposite of the negative partdefantirectifier (x): x-= K.mean (x, Axis=1, keepdims=True) x= K.l2_normalize (x, Axis=1) Pos=k.relu (x) Neg= K.relu (-x)returnK.concatenate ([Pos, neg], Axis=1)defAntirectifier_output_shape (input_shape): Shape=list (input_shape)assertLen (shape) = = 2#Only valid for 2D tensorsShape[-1] *= 2returntuple (Shape) model.add (Lambda (antirectifier, Output_shape=a

calculate the "inner product ", point-to-Point multiplication and then summation. Therefore, we must first flip the filter and pass it to conv2 to achieve our goal. In fact, I think, in fact, it does not affect the final result because W is adjusted after all. Pooling: The pooling step here is equal to that of pooldim and will not overlap. Here conv2 is used to calculate the mean value, which can optimize the performance. Remember, you do not need to activate the function here !!! This exercise

It mainly solves the number in a graph and how to flexibly change the number to achieve the goal of changing the number. The most important thing is to use the cellular array. For example, Im is a 16*20 cellular array, and the number of each cellular is 16*16*3. How can we arrange 768 of each cell in a row in order? Im is transformed into a matrix of 320*728. Each row represents a cell. Before getting started with the cellular array, I could naturally think of two for loops: A = [];T = 1;For J

Explanation question no. 1 : (1) Input-C1The 32*32 image is convolution with 6 5*5 size patches (i.e. weights, training, random initialization, adjusting during training), and 6 feature graphs are obtained.(2) S2-C3C3 that 16 10*10 size of the feature map is how to come? The S2 feature map with 1 input layer 150 (=5*5*6, not 5*5) nodes, the output layer is 16 nodes of the network for convolution.How is the value of the 3rd characteristic chart (assumed to be H3) obtained? ? First we put the net

At the disadvantage of Baidu began quietly trial new business, following the introduction of C2C business "have ah" after another e-commerce business--b2c platform "Japanese window" on-line. It is reported that Baidu and the Japanese clothing brand Nissen signed a strategic cooperation agreement. According to the agreement, the two sides will jointly carry out E-commerce business cooperation for the Chinese market. Nissen will be the first to enter the Chinese market in 2009 through the Baidu p

OpenGL window using GLUT and displays a ball with illumination.3 */4/* because the header file glut. h contains the Gl. h and Glu. H, you only need to include this file */5 # include 6 # include 78/* initialize material attributes, light source attributes, and illumination models, and open the depth buffer */9 void Init (void)10 {11 glfloat mat_specular [] = {1.0, 1.0, 1.0, 1.0 };12 glfloat mat_shininess [] ={ 50.0 };13 glfloat light_position [] = {1.0, 1.0, 1.0, 0.0 };1415 glclearcolor (0.0, 0

): Alllabels.append (Struct.unpack_from ('B', Bins,index)) Index= index + struct.calcsize ('B') returnNumpy.array (alllabels,dtype='float32') if '__main__'==__name__: Images= Loadimages ('T10k-images.idx3-ubyte') Labels= Loadlabels ('T10k-labels.idx1-ubyte') ImportMatplotlib.pyplot as Plt forXinchRange (3): Plt.figure () shown= Images[x].reshape (28,28) #shown 28*28 numpy matrixPlt.imshow (shown,cmap='Gray') Plt.title (str

Data remodeling typically uses the Reshape2 package, which is used to convert between wide data and long data, because the RESHAPE2 package is not in the default installation package for R, and needs to be installed and referenced before first use:Install.packages ("Reshape2") library (reshape2)Reshape the data by first merging the data (melt) so that each row has a unique identifier-variable combination, and then

-dimensional, 3-dimensional array for each dimension, print K # [0 1 2 3 4 5 6 7 8]print m # [[0 1 2] [3 4 5] [6 7 8]]# Transpose (matrix) Array: T property: mt[x][y] = m[y][x]print m.t # [[0 3 6] [1 4 7] [2 5 8]]# compute the inner product of the matrix Xtxprint Numpy.dot (m,m.t) # Numpy.dot dot Multiply # high dimension Axis object of the Group K = Numpy.arange (8). Reshape (2,2,2) print k # [[[0 1],[2 3]],[[4 5],[6 7]]]print k[1][0][0]# Axis transf

Suit, first look at the Python matpotlib official website http://matplotlib.org/examples/index.html on the completion of what the example, after all, their own research API too time-consuming 1, Import NumPy as NP import Matplotlib.pyplot as Plt import matplotlib.animation as animation def Data_gen (t=0 ): cnt = 0 while CNT 2, Import NumPy as NP import Matplotlib.pyplot as Plt import matplotlib.animation as animation fig = plt.figure ( def f (x, y): Return np.sin (x) + Np.

MAxpooling2d from keras.utils import np_utils import Matplotlib.pyplot as plt to keras.constraints import maxnorm from Ke Ras.optimizers Import SGD from keras.preprocessing import image import Skimage.io # fix random seed for reproducibility Seed = 7 Numpy.random.seed (SEED) # Load data (X_train, Y_train), (x_test, y_test) = Mnist.load_data () plt.subplot (221) Plt.imshow (X_train[0], Cmap=plt.get_cmap (' Gray ')) Plt.show () # Reshape to be [sampl

a collection of cities near the coast, di St2 is a city away from the ocean. Dist1 = Dist[0:5] Dist2 = dist[5:10] # Change the structure of the list, Dist1 is now a collection of 5 lists # we see NumPy () functions have the same effect reshape = [[[x] for x in dist1] Dist2 = [[x] for x in Dist2] # TEMP_MAX1 is the corresponding maximum temperature of the city in Dist1 temp_max1 = temp_max[0:5] # TEMP_MAX2 is The corresponding maximum temperature of